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All publications in 2009
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Maximum Entropy Discrimination Markov Networks
Jun Zhu and Eric P. Xing
Journal of Machine Learning Research, 2009


Reproducing Kernel Banach Spaces for Machine Learning
Haizhang Zhang, Yuesheng Xu and Jun Zhang
Journal of Machine Learning Research, 2009


On the Consistency of Feature Selection using Greedy Least Squares Regression
Tong Zhang
Journal of Machine Learning Research, 2009


Consistency and Localizability
Alon Zakai and Yaacov Ritov
Journal of Machine Learning Research, 2009


Bayesian Network Structure Learning by Recursive Autonomy Identification
Raanan Yehezkel and Boaz Lerner
Journal of Machine Learning Research, 2009


Learning Linear Ranking Functions for Beam Search with Application to Planning
Yuehua Xu, Alan Fern and Sung W. Yoon
Journal of Machine Learning Research, 2009


Robustness and Regularization of Support Vector Machines
Huan Xu, Constantine Caramanis and Shie Mannor
Journal of Machine Learning Research, 2009


Refinement of Reproducing Kernels
Yuesheng Xu and Haizhang Zhang
Journal of Machine Learning Research, 2009


Classification with Gaussians and Convex Loss
Dao-hong Xiang and Ding-xuan Zhou
Journal of Machine Learning Research, 2009


Hybrid MPI/OpenMP Parallel Linear Support Vector Machine Training
Kristian Woodsend and Jacek Gondzio
Journal of Machine Learning Research, 2009


Settable Systems: An Extension of Pearl's Causal Model with Optimization, Equilibrium, and Learning
Halbert White and Karim Chalak
Journal of Machine Learning Research, 2009


Distance Metric Learning for Large Margin Nearest Neighbor Classification
Kilian Q. Weinberger and Lawrence K. Saul
Journal of Machine Learning Research, 2009


On Efficient Large Margin Semisupervised Learning: Method and Theory
Junhui Wang, Xiaotong Shen and Wei Pan
Journal of Machine Learning Research, 2009


Using Local Dependencies within Batches to Improve Large Margin Classifiers
Volkan Vural, Glenn Fung, Balaji Krishnapuram, Jennifer G. Dy and R. B. Rao
Journal of Machine Learning Research, 2009


Prediction With Expert Advice For The Brier Game
Vladimir Vovk and Fedor Zhdanov
Journal of Machine Learning Research, 2009


Properties of Monotonic Effects on Directed Acyclic Graphs
Tyler J. Vanderweele and James M. Robins
Journal of Machine Learning Research, 2009


Feature Selection with Ensembles, Artificial Variables, and Redundancy Elimination
Eugene Tuv, Alexander Borisov, George C. Runger and Kari Torkkola
Journal of Machine Learning Research, 2009


Transfer Learning for Reinforcement Learning Domains: A Survey
Matthew E. Taylor and Peter Stone
Journal of Machine Learning Research, 2009


RL-Glue: Language-Independent Software for Reinforcement-Learning Experiments
Brian Tanner and Adam M. White
Journal of Machine Learning Research, 2009


Learning Approximate Sequential Patterns for Classification
Zeeshan Syed, Piotr Indyk and John V. Guttag
Journal of Machine Learning Research, 2009


Subgroup Analysis via Recursive Partitioning
Xiaogang Su, Chih-ling Tsai, Hansheng Wang, David M. Nickerson and Bogong Li
Journal of Machine Learning Research, 2009


Strong Limit Theorems for the Bayesian Scoring Criterion in Bayesian Networks
Nikolai Slobodianik, Dmitry Zaporozhets and Neal Madras
Journal of Machine Learning Research, 2009


The Hidden Life of Latent Variables: Bayesian Learning with Mixed Graph Models
Ricardo Silva and Zoubin Ghahramani
Journal of Machine Learning Research, 2009


Hash Kernels for Structured Data
Qinfeng Shi, James Petterson, Gideon Dror, John Langford, S. Vishwanathan and Alex J. Smola
Journal of Machine Learning Research, 2009


Nonlinear Models Using Dirichlet Process Mixtures
Babak Shahbaba and Radford M. Neal
Journal of Machine Learning Research, 2009


Python Environment for Bayesian Learning: Inferring the Structure of Bayesian Networks from Knowledge and Data
Abhik Shah and Peter J. Woolf
Journal of Machine Learning Research, 2009


The P-Norm Push: A Simple Convex Ranking Algorithm that Concentrates at the Top of the List
Cynthia Rudin
Journal of Machine Learning Research, 2009


Margin-based Ranking and an Equivalence between AdaBoost and RankBoost
Cynthia Rudin and Robert E. Schapire
Journal of Machine Learning Research, 2009


Bi-Level Path Following for Cross Validated Solution of Kernel Quantile Regression
Saharon Rosset
Journal of Machine Learning Research, 2009


Deterministic Error Analysis of Support Vector Regression and Related Regularized Kernel Methods
Christian Rieger and Barbara Zwicknagl
Journal of Machine Learning Research, 2009


Polynomial-Delay Enumeration of Monotonic Graph Classes
Jan Ramon and Siegfried Nijssen
Journal of Machine Learning Research, 2009


Model Monitor ({\it M}$^{\mbox{2}}$): Evaluating, Comparing, and Monitoring Models
Troy Raeder and Nitesh V. Chawla
Journal of Machine Learning Research, 2009


Estimating Labels from Label Proportions
Novi Quadrianto, Alex J. Smola, Tibério S. Caetano and Quoc V. Le
Journal of Machine Learning Research, 2009


An Algorithm for Reading Dependencies from the Minimal Undirected Independence Map of a Graphoid that Satisfies Weak Transitivity
José M. Peña, Roland Nilsson, Johan Björkegren and Jesper Tegnér
Journal of Machine Learning Research, 2009


Perturbation Corrections in Approximate Inference: Mixture Modelling Applications
Ulrich Paquet, Ole Winther and Manfred Opper
Journal of Machine Learning Research, 2009


Bounded Kernel-Based Online Learning
Francesco Orabona, Joseph Keshet and Barbara Caputo
Journal of Machine Learning Research, 2009


Supervised Descriptive Rule Discovery: A Unifying Survey of Contrast Set, Emerging Pattern and Subgroup Mining
Petra K. Novak, Nada Lavrac and Geoffrey I. Webb
Journal of Machine Learning Research, 2009


Distributed Algorithms for Topic Models
David Newman, Arthur U. Asuncion, Padhraic Smyth and Max Welling
Journal of Machine Learning Research, 2009


Cautious Collective Classification
Luke Mcdowell, Kalyan M. Gupta and David W. Aha
Journal of Machine Learning Research, 2009


Nonextensive Information Theoretic Kernels on Measures
André Martins, Noah A. Smith, Eric P. Xing, Pedro Aguiar and Mário Figueiredo
Journal of Machine Learning Research, 2009


Online Learning with Sample Path Constraints
Shie Mannor, John N. Tsitsiklis and Jia Y. Yu
Journal of Machine Learning Research, 2009


Learning When Concepts Abound
Omid Madani, Michael Connor and Wiley Greiner
Journal of Machine Learning Research, 2009


The Nonparanormal: Semiparametric Estimation of High Dimensional Undirected Graphs
Han Liu, John D. Lafferty and Larry A. Wasserman
Journal of Machine Learning Research, 2009


Marginal Likelihood Integrals for Mixtures of Independence Models
Shaowei Lin, Bernd Sturmfels and Zhiqiang Xu
Journal of Machine Learning Research, 2009


Multi-task Reinforcement Learning in Partially Observable Stochastic Environments
Hui Li, Xuejun Liao and Lawrence Carin
Journal of Machine Learning Research, 2009


Controlling the False Discovery Rate of the Association/Causality Structure Learned with the PC Algorithm
Junning Li and Z. J. Wang
Journal of Machine Learning Research, 2009


DL-Learner: Learning Concepts in Description Logics
Jens Lehmann
Journal of Machine Learning Research, 2009


Exploring Strategies for Training Deep Neural Networks
Hugo Larochelle, Yoshua Bengio, Jérôme Louradour and Pascal Lamblin
Journal of Machine Learning Research, 2009


Sparse Online Learning via Truncated Gradient
John Langford, Lihong Li and Tong Zhang
Journal of Machine Learning Research, 2009


An Analysis of Convex Relaxations for MAP Estimation of Discrete MRFs
M. P. Kumar, Vladimir Kolmogorov and Philip Torr
Journal of Machine Learning Research, 2009


Low-Rank Kernel Learning with Bregman Matrix Divergences
Brian Kulis, Mátyás A. Sustik and Inderjit S. Dhillon
Journal of Machine Learning Research, 2009


Universal Kernel-Based Learning with Applications to Regular Languages
Leonid Kontorovich and Boaz Nadler
Journal of Machine Learning Research, 2009


Learning Halfspaces with Malicious Noise
Adam R. Klivans, Philip M. Long and Rocco A. Servedio
Journal of Machine Learning Research, 2009


Dlib-ml: A Machine Learning Toolkit
Davis E. King
Journal of Machine Learning Research, 2009


Markov Properties for Linear Causal Models with Correlated Errors
Changsung Kang and Jin Tian
Journal of Machine Learning Research, 2009


A Least-squares Approach to Direct Importance Estimation
Takafumi Kanamori, Shohei Hido and Masashi Sugiyama
Journal of Machine Learning Research, 2009


On Uniform Deviations of General Empirical Risks with Unboundedness, Dependence, and High Dimensionality
Wenxin Jiang
Journal of Machine Learning Research, 2009


Structure Spaces
Brijnesh J. Jain and Klaus Obermayer
Journal of Machine Learning Research, 2009


Fourier Theoretic Probabilistic Inference over Permutations
Jonathan Huang, Carlos Guestrin and Leonidas J. Guibas
Journal of Machine Learning Research, 2009


Online Learning with Samples Drawn from Non-identical Distributions
Ting Hu and Ding-xuan Zhou
Journal of Machine Learning Research, 2009


Learning Permutations with Exponential Weights
David P. Helmbold and Manfred K. Warmuth
Journal of Machine Learning Research, 2009


Exploiting Product Distributions to Identify Relevant Variables of Correlation Immune Functions
Lisa Hellerstein, Bernard Rosell, Eric Bach, Soumya Ray and David Page
Journal of Machine Learning Research, 2009


Entropy Inference and the James-Stein Estimator, with Application to Nonlinear Gene Association Networks
Jean Hausser and Korbinian Strimmer
Journal of Machine Learning Research, 2009


A Survey of Accuracy Evaluation Metrics of Recommendation Tasks
Asela Gunawardana and Guy Shani
Journal of Machine Learning Research, 2009


Application of Non Parametric Empirical Bayes Estimation to High Dimensional Classification
Eitan Greenshtein and Junyong Park
Journal of Machine Learning Research, 2009


Evolutionary Model Type Selection for Global Surrogate Modeling
Dirk Gorissen, Tom Dhaene and Filip D. Turck
Journal of Machine Learning Research, 2009


Robust Process Discovery with Artificial Negative Events
Stijn Goedertier, David Martens, Jan Vanthienen and Bart Baesens
Journal of Machine Learning Research, 2009


NEUROSVM: An Architecture to Reduce the Effect of the Choice of Kernel on the Performance of SVM
Pradip Ghanty, Samrat Paul and Nikhil R. Pal
Journal of Machine Learning Research, 2009


Optimized Cutting Plane Algorithm for Large-Scale Risk Minimization
Vojtech Franc and Sören Sonnenburg
Journal of Machine Learning Research, 2009


Stable and Efficient Gaussian Process Calculations
Leslie Foster, Alex Waagen, Nabeela Aijaz, Michael Hurley, Apolonio Luis, Joel Rinsky, Chandrika Satyavolu, Michael J. Way, Paul R. Gazis and Ashok Srivastava
Journal of Machine Learning Research, 2009


Estimation of Sparse Binary Pairwise Markov Networks using Pseudo-likelihoods
Holger Höfling and Robert Tibshirani
Journal of Machine Learning Research, 2009


An Anticorrelation Kernel for Subsystem Training in Multiple Classifier Systems
Luciana Ferrer, M. K. Sönmez and Elizabeth Shriberg
Journal of Machine Learning Research, 2009


On The Power of Membership Queries in Agnostic Learning
Vitaly Feldman
Journal of Machine Learning Research, 2009


Ultrahigh Dimensional Feature Selection: Beyond The Linear Model
Jianqing Fan, Richard Samworth and Yichao Wu
Journal of Machine Learning Research, 2009


CarpeDiem: Optimizing the Viterbi Algorithm and Applications to Supervised Sequential Learning
Roberto Esposito and Daniele P. Radicioni
Journal of Machine Learning Research, 2009


Particle Swarm Model Selection
Hugo J. Escalante, Manuel Montes-y-gómez and Luis E. Sucar
Journal of Machine Learning Research, 2009


Incorporating Functional Knowledge in Neural Networks
Charles Dugas, Yoshua Bengio, Françcois Bélisle, Claude Nadeau and René Garcia
Journal of Machine Learning Research, 2009


Efficient Online and Batch Learning Using Forward Backward Splitting
John C. Duchi and Yoram Singer
Journal of Machine Learning Research, 2009


Computing Maximum Likelihood Estimates in Recursive Linear Models with Correlated Errors
Mathias Drton, Michael Eichler and Thomas S. Richardson
Journal of Machine Learning Research, 2009


Analysis of Perceptron-Based Active Learning
Sanjoy Dasgupta, Adam T. Kalai and Claire Monteleoni
Journal of Machine Learning Research, 2009


Identification of Recurrent Neural Networks by Bayesian Interrogation Techniques
Barnabás Póczos and András Lörincz
Journal of Machine Learning Research, 2009


Scalable Collaborative Filtering Approaches for Large Recommender Systems
Gábor Takács, István Pilászy, Bottyán Németh and Domonkos Tikk
Journal of Machine Learning Research, 2009


Learning Nondeterministic Classifiers
Juan Coz, Jorge D\'ıez and Antonio Bahamonde
Journal of Machine Learning Research, 2009


Fast Approximate {\it k}NN Graph Construction for High Dimensional Data via Recursive Lanczos Bisection
Jie Chen, Haw-ren Fang and Yousef Saad
Journal of Machine Learning Research, 2009


Similarity-based Classification: Concepts and Algorithms
Yihua Chen, Eric K. Garcia, Maya R. Gupta, Ali Rahimi and Luca Cazzanti
Journal of Machine Learning Research, 2009


Nearest Neighbor Clustering: A Baseline Method for Consistent Clustering with Arbitrary Objective Functions
Sébastien Bubeck and Ulrike V. Luxburg
Journal of Machine Learning Research, 2009


Provably Efficient Learning with Typed Parametric Models
Emma Brunskill, Bethany R. Leffler, Lihong Li, Michael L. Littman and Nicholas Roy
Journal of Machine Learning Research, 2009


Improving the Reliability of Causal Discovery from Small Data Sets Using Argumentation
Facundo Bromberg and Dimitris Margaritis
Journal of Machine Learning Research, 2009


SGD-QN: Careful Quasi-Newton Stochastic Gradient Descent
Antoine Bordes, Léon Bottou and Patrick Gallinari
Journal of Machine Learning Research, 2009


Adaptive False Discovery Rate Control under Independence and Dependence
Gilles Blanchard and Étienne Roquain
Journal of Machine Learning Research, 2009


Discriminative Learning Under Covariate Shift
Steffen Bickel, Michael Brückner and Tobias Scheffer
Journal of Machine Learning Research, 2009


Data-driven Calibration of Penalties for Least-Squares Regression
Sylvain Arlot and Pascal Massart
Journal of Machine Learning Research, 2009


When Is There a Representer Theorem? Vector Versus Matrix Regularizers
Andreas Argyriou, Charles A. Micchelli and Massimiliano Pontil
Journal of Machine Learning Research, 2009


Learning Acyclic Probabilistic Circuits Using Test Paths
Dana Angluin, James Aspnes, Jiang Chen, David Eisenstat and Lev Reyzin
Journal of Machine Learning Research, 2009


Reinforcement Learning in Finite MDPs: PAC Analysis
Alexander L. Strehl, Lihong Li and Michael L. Littman
Journal of Machine Learning Research, 2009


Generalization Bounds for Ranking Algorithms via Algorithmic Stability
Shivani Agarwal and Partha Niyogi
Journal of Machine Learning Research, 2009


A New Approach to Collaborative Filtering: Operator Estimation with Spectral Regularization
Jacob Abernethy, Theodoros Evgeniou, Jean-philippe Vert and Francis R. Bach
Journal of Machine Learning Research, 2009


Java-ML: A Machine Learning Library
Thomas Abeel, Yves Peer and Yvan Saeys
Journal of Machine Learning Research, 2009


Nieme: Large-Scale Energy-Based Models
Francis Maes
Journal of Machine Learning Research, 2009


A Parameter-Free Classification Method for Large Scale Learning
Marc Boullé
Journal of Machine Learning Research, 2009


SimpleNPKL: simple non-parametric kernel learning
Jinfeng Zhuang, Ivor W. Tsang and Steven C. Hoi
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


On primal and dual sparsity of Markov networks
Jun Zhu and Eric P. Xing
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


MedLDA: maximum margin supervised topic models for regression and classification
Jun Zhu, Amr Ahmed and Eric P. Xing
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Multi-instance learning by treating instances as non-I.I.D. samples
Zhi-hua Zhou, Yu-yin Sun and Yu-feng Li
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Learning non-redundant codebooks for classifying complex objects
Wei Zhang, Akshat Surve, Thomas G. Dietterich and Xiaoli Z. Fern
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Prototype vector machine for large scale semi-supervised learning
Kai Zhang, James T. Kwok and Bahram Parvin
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Learning instance specific distances using metric propagation
De-chuan Zhan, Ming Li, Yu-feng Li and Zhi-hua Zhou
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Discovering options from example trajectories
Peng Zang, Peng Zhou, David Minnen and Charles Jr.
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Compositional noisy-logical learning
Alan L. Yuille and Songfeng Zheng
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Interactively optimizing information retrieval systems as a dueling bandits problem
Yisong Yue and Thorsten Joachims
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Robust feature extraction via information theoretic learning
Xiaotong Yuan and Bao-gang Hu
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Workshop summary: Workshop on learning feature hierarchies
Kay Yu, Ruslan Salakhutdinov, Yann Lecun, Geoffrey E. Hinton and Yoshua Bengio
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Piecewise-stationary bandit problems with side observations
Jia Y. Yu and Shie Mannor
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Large-scale collaborative prediction using a nonparametric random effects model
Kai Yu, John D. Lafferty, Shenghuo Zhu and Yihong Gong
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Learning structural SVMs with latent variables
Chun-nam J. Yu and Thorsten Joachims
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Stochastic search using the natural gradient
Yi Sun, Daan Wierstra, Tom Schaul and Jürgen Schmidhuber
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Online learning by ellipsoid method
Liu Yang, Rong Jin and Jieping Ye
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Optimal reverse prediction: a unified perspective on supervised, unsupervised and semi-supervised learning
Linli Xu, Martha White and Dale Schuurmans
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Non-monotonic feature selection
Zenglin Xu, Rong Jin, Jieping Ye, Michael R. Lyu and Irwin King
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


A stochastic memoizer for sequence data
Frank Wood, Cédric Archambeau, Jan Gasthaus, Lancelot James and Yee W. Teh
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Workshop summary: Results of the 2009 reinforcement learning competition
David Wingate, Carlos Diuk, Lihong Li, Matthew Taylor and Jordan Frank
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Herding dynamical weights to learn
Max Welling
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Feature hashing for large scale multitask learning
Kilian Q. Weinberger, Anirban Dasgupta, John Langford, Josh Attenberg and Alex J. Smola
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Tutorial summary: Survey of boosting from an optimization perspective
Manfred K. Warmuth and S.v.n. Vishwanathan
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Evaluation methods for topic models
Hanna M. Wallach, Iain Murray, Ruslan Salakhutdinov and David M. Mimno
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


K-means in space: a radiation sensitivity evaluation
Kiri L. Wagstaff and Benjamin J. Bornstein
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


BoltzRank: learning to maximize expected ranking gain
Maksims Volkovs and Richard S. Zemel
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Model-free reinforcement learning as mixture learning
Marc Toussaint and Nikos A. Vlassis
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


More generality in efficient multiple kernel learning
Manik Varma and Bodla R. Babu
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Ranking with ordered weighted pairwise classification
Nicolas Usunier, David Buffoni and Patrick Gallinari
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Tutorial summary: Learning with dependencies between several response variables
Volker Tresp and Kai Yu
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Robot trajectory optimization using approximate inference
Marc Toussaint
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Structure learning with independent non-identically distributed data
Robert E. Tillman
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Using fast weights to improve persistent contrastive divergence
Tijmen Tieleman and Geoffrey E. Hinton
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Kernelized value function approximation for reinforcement learning
Gavin Taylor and Ronald Parr
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Factored conditional restricted Boltzmann Machines for modeling motion style
Graham W. Taylor and Geoffrey E. Hinton
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Discriminative {\it k}-metrics
Guillermo Sapiro and Arthur D. Szlam
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Optimistic initialization and greediness lead to polynomial time learning in factored MDPs
Istvan Szita and András Lörincz
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Fast gradient-descent methods for temporal-difference learning with linear function approximation
Richard S. Sutton, Hamid R. Maei, Doina Precup, Shalabh Bhatnagar, David Silver, Csaba Szepesvári and Eric Wiewiora
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


A simpler unified analysis of budget perceptrons
Ilya Sutskever
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


A least squares formulation for a class of generalized eigenvalue problems in machine learning
Liang Sun, Shuiwang Ji and Jieping Ye
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Multi-assignment clustering for Boolean data
Andreas P. Streich, Mario Frank, David A. Basin and Joachim M. Buhmann
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Hilbert space embeddings of conditional distributions with applications to dynamical systems
Le Song, Jonathan Huang, Kenji Fukumizu and Alex J. Smola
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Tutorial summary: Structured prediction for natural language processing
Noah A. Smith
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Uncertainty sampling and transductive experimental design for active dual supervision
Vikas Sindhwani, Prem Melville and Richard D. Lawrence
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Workshop summary: Abstraction in reinforcement learning
Özgür Simsek
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Monte-Carlo simulation balancing
David Silver and Gerald Tesauro
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Structure preserving embedding
Blake Shaw and Tony Jebara
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Stochastic methods for {\it l}$_{\mbox{1}}$ regularized loss minimization
Shai Shalev-shwartz and Ambuj Tewari
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Workshop summary: Numerical mathematics in machine learning
Matthias Seeger, Suvrit Sra and John P. Cunningham
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Function factorization using warped Gaussian processes
Mikkel N. Schmidt
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Ranking interesting subgroups
Stefan Rüping
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Learning structurally consistent undirected probabilistic graphical models
Sushmita Roy, Terran Lane and Margaret Werner-washburne
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Surrogate regret bounds for proper losses
Mark D. Reid and Robert C. Williamson
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Supervised learning from multiple experts: whom to trust when everyone lies a bit
Vikas C. Raykar, Shipeng Yu, Linda H. Zhao, Anna K. Jerebko, Charles Florin, Gerardo H. Valadez, Luca Bogoni and Linda Moy
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


The Bayesian group-Lasso for analyzing contingency tables
Sudhir Raman, Thomas J. Fuchs, Peter J. Wild, Edgar Dahl and Volker Roth
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Large-scale deep unsupervised learning using graphics processors
Rajat Raina, Anand Madhavan and Andrew Y. Ng
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Nearest neighbors in high-dimensional data: the emergence and influence of hubs
Milos Radovanovic, Alexandros Nanopoulos and Mirjana Ivanovic
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


An efficient projection for {\it l}$_{\mbox{1}}$,$_{\mbox{infinity}}$ regularization
Ariadna Quattoni, Xavier Carreras, Michael Collins and Trevor Darrell
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Sparse higher order conditional random fields for improved sequence labeling
Xian Qian, Xiaoqian Jiang, Qi Zhang, Xuanjing Huang and Lide Wu
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


An efficient sparse metric learning in high-dimensional space via {\it l}$_{\mbox{1}}$-penalized log-determinant regularization
Guo-jun Qi, Jinhui Tang, Zheng-jun Zha, Tat-seng Chua and Hong-jiang Zhang
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Independent factor topic models
Duangmanee Putthividhya, Hagai T. Attias and Srikantan S. Nagarajan
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Learning when to stop thinking and do something!
Barnabás Póczos, Yasin Abbasi-yadkori, Csaba Szepesvári, Russell Greiner and Nathan R. Sturtevant
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Multi-class image segmentation using conditional random fields and global classification
Nils Plath, Marc Toussaint and Shinichi Nakajima
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Constraint relaxation in approximate linear programs
Marek Petrik and Shlomo Zilberstein
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Detecting the direction of causal time series
Jonas Peters, Dominik Janzing, Arthur Gretton and Bernhard Schölkopf
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Binary action search for learning continuous-action control policies
Jason Pazis and Michail G. Lagoudakis
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Unsupervised hierarchical modeling of locomotion styles
Wei Pan and Lorenzo Torresani
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Nonparametric factor analysis with beta process priors
John W. Paisley and Lawrence Carin
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Solution stability in linear programming relaxations: graph partitioning and unsupervised learning
Sebastian Nowozin and Stefanie Jegelka
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Tutorial summary: The neuroscience of reinforcement learning
Yael Niv
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Convex variational Bayesian inference for large scale generalized linear models
Hannes Nickisch and Matthias Seeger
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Information theoretic measures for clusterings comparison: is a correction for chance necessary?
Xuan V. Nguyen, Julien Epps and James Bailey
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Learning complex motions by sequencing simpler motion templates
Gerhard Neumann, Wolfgang Maass and Jan R. Peters
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Workshop summary: Automated interpretation and modelling of cell images
Robert F. Murphy, Chun-nan Hsu and Loris Nanni
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Regression by dependence minimization and its application to causal inference in additive noise models
Joris M. Mooij, Dominik Janzing, Jonas Peters and Bernhard Schölkopf
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Deep learning from temporal coherence in video
Hossein Mobahi, Ronan Collobert and Jason Weston
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Bandit-based optimization on graphs with application to library performance tuning
Frédéric D. Mesmay, Arpad Rimmel, Yevgen Voronenko and Markus Püschel
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Partial order embedding with multiple kernels
Brian Mcfee and Gert R. Lanckriet
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Polyhedral outer approximations with application to natural language parsing
André Martins, Noah A. Smith and Eric P. Xing
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Sparse Gaussian graphical models with unknown block structure
Benjamin M. Marlin and Kevin P. Murphy
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Proto-predictive representation of states with simple recurrent temporal-difference networks
Takaki Makino
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Online dictionary learning for sparse coding
Julien Mairal, Jean Ponce, Guillermo Sapiro and Francis R. Bach
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Identifying suspicious URLs: an application of large-scale online learning
Justin Ma, Lawrence K. Saul, Stefan Savage and Geoffrey M. Voelker
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Geometry-aware metric learning
Zhengdong Lu, Prateek Jain and Inderjit S. Dhillon
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Efficient Euclidean projections in linear time
Jun Liu and Jieping Ye
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Blockwise coordinate descent procedures for the multi-task lasso, with applications to neural semantic basis discovery
Han Liu, Mark Palatucci and Jian Zhang
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Topic-link LDA: joint models of topic and Author community
Yan Liu, Alexandru Niculescu-mizil and Wojciech Gryc
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Learning from measurements in exponential families
Percy Liang, Michael I. Jordan and Dan Klein
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Transfer learning for collaborative filtering via a rating-matrix generative model
Bin Li, Qiang Yang and Xiangyang Xue
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Semi-supervised learning using label mean
Yu-feng Li, James T. Kwok and Zhi-hua Zhou
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


ABC-boost: adaptive base class boost for multi-class classification
Ping Li
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Tutorial summary: Large social and information networks: opportunities for ML
Jure Leskovec
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations
Honglak Lee, Roger Grosse, Rajesh Ranganath and Andrew Y. Ng
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Non-linear matrix factorization with Gaussian processes
Neil D. Lawrence and Raquel Urtasun
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Learning nonlinear dynamic models
John Langford, Ruslan Salakhutdinov and Tong Zhang
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Approximate inference for planning in stochastic relational worlds
Tobias Lang and Marc Toussaint
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Generalization analysis of listwise learning-to-rank algorithms
Yanyan Lan, Tie-yan Liu, Zhiming Ma and Hang Li
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Block-wise construction of acyclic relational features with monotone irreducibility and relevancy properties
Ondrej Kuzelka and Filip Zelezn\'y
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Learning spectral graph transformations for link prediction
Jérôme Kunegis and Andreas Lommatzsch
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


On sampling-based approximate spectral decomposition
Sanjiv Kumar, Mehryar Mohri and Ameet Talwalkar
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Multiple indefinite kernel learning with mixed norm regularization
Matthieu Kowalski, Marie Szafranski and Liva Ralaivola
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Rule learning with monotonicity constraints
Wojciech Kotlowski and Roman Slowinski
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


The graphlet spectrum
Risi Kondor, Nino Shervashidze and Karsten M. Borgwardt
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Regularization and feature selection in least-squares temporal difference learning
J. Z. Kolter and Andrew Y. Ng
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Near-Bayesian exploration in polynomial time
J. Z. Kolter and Andrew Y. Ng
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Learning Markov logic network structure via hypergraph lifting
Stanley Kok and Pedro Domingos
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Boosting products of base classifiers
Balázs Kégl and Róbert Busa-fekete
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Learning prediction suffix trees with Winnow
Nikolaos Karampatziakis and Dexter Kozen
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


A Bayesian approach to protein model quality assessment
Hetunandan Kamisetty and Christopher J. Langmead
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Orbit-product representation and correction of Gaussian belief propagation
Jason K. Johnson, Vladimir Y. Chernyak and Michael Chertkov
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


An accelerated gradient method for trace norm minimization
Shuiwang Ji and Jieping Ye
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Trajectory prediction: learning to map situations to robot trajectories
Nikolay Jetchev and Marc Toussaint
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Graph construction and {\it b}-matching for semi-supervised learning
Tony Jebara, Jun Wang and Shih-fu Chang
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Group lasso with overlap and graph lasso
Laurent Jacob, Jean-philippe Vert and Guillaume R. Obozinski
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Learning with structured sparsity
Junzhou Huang, Tong Zhang and Dimitris N. Metaxas
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Learning linear dynamical systems without sequence information
Tzu-kuo Huang and Jeff G. Schneider
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Partially supervised feature selection with regularized linear models
Thibault Helleputte and Pierre Dupont
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Hoeffding and Bernstein races for selecting policies in evolutionary direct policy search
Verena Heidrich-meisner and Christian Igel
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Efficient learning algorithms for changing environments
Elad Hazan and C. Seshadhri
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Bayesian clustering for email campaign detection
Peter Haider and Tobias Scheffer
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Bayesian inference for Plackett-Luce ranking models
John Guiver and Edward Snelson
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Dynamic analysis of multiagent {\it Q}-learning with {\&}epsilon;-greedy exploration
Eduardo R. Gomes and Ryszard Kowalczyk
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Fast evolutionary maximum margin clustering
Fabian Gieseke, Tapio Pahikkala and Oliver Kramer
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


PAC-Bayesian learning of linear classifiers
Pascal Germain, Alexandre Lacasse, Mario Marchand and François Laviolette
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Sequential Bayesian prediction in the presence of changepoints
Roman Garnett, Stephen J. Roberts and Michael Osborne
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Gradient descent with sparsification: an iterative algorithm for sparse recovery with restricted isometry property
Rahul Garg and Rohit Khandekar
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Dynamic mixed membership blockmodel for evolving networks
Wenjie Fu, Le Song and Eric P. Xing
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Invited talk: Drifting games, boosting and online learning
Yoav Freund
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


A majorization-minimization algorithm for (multiple) hyperparameter learning
Chuan-sheng Foo, Chuong B. Do and Andrew Y. Ng
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


GAODE and HAODE: two proposals based on AODE to deal with continuous variables
M. J. Flores, José A. Gámez, Ana M. Mart\'ınez and Jose M. Puerta
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Learning to segment from a few well-selected training images
Alireza Farhangfar, Russell Greiner and Csaba Szepesvári
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Tutorial summary: Convergence of natural dynamics to equilibria
Eyal Even-dar and Vahab S. Mirrokni
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Workshop summary: Sparse methods for music audio
Douglas Eck, Dan Ellis and Philippe Hamel
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Boosting with structural sparsity
John C. Duchi and Yoram Singer
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Domain adaptation from multiple sources via auxiliary classifiers
Lixin Duan, Ivor W. Tsang, Dong Xu and Tat-seng Chua
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Workshop summary: The fourth workshop on evaluation methods for machine learning
Chris Drummond, Nathalie Japkowicz, William Klement and Sofus A. Macskassy
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Accounting for burstiness in topic models
Gabriel Doyle and Charles Elkan
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Accelerated sampling for the Indian Buffet Process
Finale Doshi-velez and Zoubin Ghahramani
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Proximal regularization for online and batch learning
Chuong B. Do, Quoc V. Le and Chuan-sheng Foo
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Large margin training for hidden Markov models with partially observed states
Trinh Do and Thierry Arti\`eres
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


The adaptive {\it k}-meteorologists problem and its application to structure learning and feature selection in reinforcement learning
Carlos Diuk, Lihong Li and Bethany R. Leffler
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


A scalable framework for discovering coherent co-clusters in noisy data
Meghana Deodhar, Gunjan Gupta, Joydeep Ghosh, Hyuk Cho and Inderjit S. Dhillon
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Good learners for evil teachers
Ofer Dekel and Ohad Shamir
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Analytic moment-based Gaussian process filtering
Marco F. Huber, Uwe D. Hanebeck and Marc Deisenroth
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Deep transfer via second-order Markov logic
Jesse Davis and Pedro Domingos
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Unsupervised search-based structured prediction
Hal Daume
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Tutorial summary: Active learning
Sanjoy Dasgupta and John Langford
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Fitting a graph to vector data
Samuel I. Daitch, Jonathan A. Kelner and Daniel A. Spielman
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


EigenTransfer: a unified framework for transfer learning
Wenyuan Dai, Ou Jin, Gui-rong Xue, Qiang Yang and Yong Yu
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Invited talk: Can learning kernels help performance?
Corinna Cortes
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Nonparametric estimation of the precision-recall curve
Nicolas Vayatis and Stéphan J. Clémençcon
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Exploiting sparse Markov {\it and} covariance structure in multiresolution models
Myung J. Choi, Venkat Chandrasekaran and Alan S. Willsky
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Learning dictionaries of stable autoregressive models for audio scene analysis
Youngmin Cho and Lawrence K. Saul
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Matrix updates for perceptron training of continuous density hidden Markov models
Chih-chieh Cheng, Fei Sha and Lawrence K. Saul
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Decision tree and instance-based learning for label ranking
Weiwei Cheng, Jens C. Huhn and Eyke Hüllermeier
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


A convex formulation for learning shared structures from multiple tasks
Jianhui Chen, Lei Tang, Jun Liu and Jieping Ye
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Learning kernels from indefinite similarities
Yihua Chen, Benjamin Recht and Maya Gupta
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Multi-view clustering via canonical correlation analysis
Kamalika Chaudhuri, Sham M. Kakade, Karen Livescu and Karthik Sridharan
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Robust bounds for classification via selective sampling
Nicolò Cesa-bianchi, Claudio Gentile and Francesco Orabona
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Structure learning of Bayesian networks using constraints
Cassio Campos, Zhi Zeng and Qiang Ji
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Probabilistic dyadic data analysis with local and global consistency
Deng Cai, Xuanhui Wang and Xiaofei He
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Optimized expected information gain for nonlinear dynamical systems
Alberto G. Busetto, Cheng S. Ong and Joachim M. Buhmann
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Active learning for directed exploration of complex systems
Michael C. Burl and Esther Wang
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Spectral clustering based on the graph {\it p}-Laplacian
Thomas Bühler and Matthias Hein
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Online feature elicitation in interactive optimization
Craig Boutilier, Kevin Regan and Paolo Viappiani
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Predictive representations for policy gradient in POMDPs
Abdeslam Boularias and Brahim Chaib-draa
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Split variational inference
Guillaume Bouchard and Onno Zoeter
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Tutorial summary: Reductions in machine learning
Alina Beygelzimer, John Langford and Bianca Zadrozny
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Importance weighted active learning
Alina Beygelzimer, Sanjoy Dasgupta and John Langford
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Tutorial summary: Machine learning in IR: recent successes and new opportunities
Paul N. Bennett, Misha Bilenko and Kevyn Collins-thompson
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Curriculum learning
Yoshua Bengio, Jérôme Louradour, Ronan Collobert and Jason Weston
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Grammatical inference as a principal component analysis problem
Rapha\"el Bailly, Françcois Denis and Liva Ralaivola
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Workshop summary: On-line learning with limited feedback
Jean-yves Audibert, Peter Auer, Alessandro Lazaric, Rémi Munos, Daniil Ryabko and Csaba Szepesvári
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Incorporating domain knowledge into topic modeling via Dirichlet Forest priors
David Andrzejewski, Xiaojin Zhu and Mark Craven
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Route kernels for trees
Fabio Aiolli, Giovanni Martino and Alessandro Sperduti
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Workshop summary: Seventh annual workshop on Bayes applications
John M. Agosta, Russell Almond, Dennis M. Buede, Marek J. Druzdzel, Judy Goldsmith and Silja Renooij
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Tractable nonparametric Bayesian inference in Poisson processes with Gaussian process intensities
Ryan P. Adams, Iain Murray and David Mackay
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Archipelago: nonparametric Bayesian semi-supervised learning
Ryan P. Adams and Zoubin Ghahramani
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Measuring model complexity with the prior predictive
Wolf Vanpaemel
Advances in Neural Information Processing Systems 22, 2009


Bilinear classifiers for visual recognition
Hamed Pirsiavash, Deva Ramanan and Charless C. Fowlkes
Advances in Neural Information Processing Systems 22, 2009


White Functionals for Anomaly Detection in Dynamical Systems
Marco Cuturi, Jean-philippe Vert and Alexandre D'aspremont
Advances in Neural Information Processing Systems 22, 2009


Help or Hinder: Bayesian Models of Social Goal Inference
Tomer Ullman, Chris Baker, Owen Macindoe, Owain Evans, Noah Goodman and Joshua B. Tenenbaum
Advances in Neural Information Processing Systems 22, 2009


Solving Stochastic Games
Liam M. Dermed and Charles L. Isbell
Advances in Neural Information Processing Systems 22, 2009


Submanifold density estimation
Arkadas Ozakin and Alexander G. Gray
Advances in Neural Information Processing Systems 22, 2009


Unsupervised feature learning for audio classification using convolutional deep belief networks
Honglak Lee, Peter Pham, Yan Largman and Andrew Y. Ng
Advances in Neural Information Processing Systems 22, 2009


Compositionality of optimal control laws
Emanuel Todorov
Advances in Neural Information Processing Systems 22, 2009


Periodic Step Size Adaptation for Single Pass On-line Learning
Chun-nan Hsu, Yu-ming Chang, Hanshen Huang and Yuh-jye Lee
Advances in Neural Information Processing Systems 22, 2009


Who’s Doing What: Joint Modeling of Names and Verbs for Simultaneous Face and Pose Annotation
Jie Luo, Barbara Caputo and Vittorio Ferrari
Advances in Neural Information Processing Systems 22, 2009


Structural inference affects depth perception in the context of potential occlusion
Ian Stevenson and Konrad Koerding
Advances in Neural Information Processing Systems 22, 2009


Multi-Label Prediction via Sparse Infinite CCA
Piyush Rai and Hal Daume
Advances in Neural Information Processing Systems 22, 2009


Learning transport operators for image manifolds
Benjamin Culpepper and Bruno A. Olshausen
Advances in Neural Information Processing Systems 22, 2009


Filtering Abstract Senses From Image Search Results
Kate Saenko and Trevor Darrell
Advances in Neural Information Processing Systems 22, 2009


Exploring Functional Connectivities of the Human Brain using Multivariate Information Analysis
Barry Chai, Dirk Walther, Diane Beck and Li Fei-fei
Advances in Neural Information Processing Systems 22, 2009


Convergent Temporal-Difference Learning with Arbitrary Smooth Function Approximation
Shalabh Bhatnagar, Doina Precup, David Silver, Richard S. Sutton, Hamid R. Maei and Csaba Szepesvári
Advances in Neural Information Processing Systems 22, 2009


A Sparse Non-Parametric Approach for Single Channel Separation of Known Sounds
Paris Smaragdis, Madhusudana Shashanka and Bhiksha Raj
Advances in Neural Information Processing Systems 22, 2009


A Parameter-free Hedging Algorithm
Kamalika Chaudhuri, Yoav Freund and Daniel J. Hsu
Advances in Neural Information Processing Systems 22, 2009


Posterior vs Parameter Sparsity in Latent Variable Models
Kuzman Ganchev, Ben Taskar, Fernando Pereira and João Gama
Advances in Neural Information Processing Systems 22, 2009


Learning to Explore and Exploit in POMDPs
Chenghui Cai, Xuejun Liao and Lawrence Carin
Advances in Neural Information Processing Systems 22, 2009


An Infinite Factor Model Hierarchy Via a Noisy-Or Mechanism
Douglas Eck, Yoshua Bengio and Aaron C. Courville
Advances in Neural Information Processing Systems 22, 2009


Neural Implementation of Hierarchical Bayesian Inference by Importance Sampling
Lei Shi and Thomas L. Griffiths
Advances in Neural Information Processing Systems 22, 2009


Lower bounds on minimax rates for nonparametric regression with additive sparsity and smoothness
Garvesh Raskutti, Bin Yu and Martin J. Wainwright
Advances in Neural Information Processing Systems 22, 2009


Streaming k-means approximation
Nir Ailon, Ragesh Jaiswal and Claire Monteleoni
Advances in Neural Information Processing Systems 22, 2009


Hierarchical Learning of Dimensional Biases in Human Categorization
Adam Sanborn, Nick Chater and Katherine A. Heller
Advances in Neural Information Processing Systems 22, 2009


Sequential effects reflect parallel learning of multiple environmental regularities
Matthew Wilder, Matt Jones and Michael C. Mozer
Advances in Neural Information Processing Systems 22, 2009


Subject independent EEG-based BCI decoding
Siamac Fazli, Cristian Grozea, Marton Danoczy, Benjamin Blankertz, Florin Popescu and Klaus-robert Müller
Advances in Neural Information Processing Systems 22, 2009


Accelerating Bayesian Structural Inference for Non-Decomposable Gaussian Graphical Models
Baback Moghaddam, Emtiyaz Khan, Kevin P. Murphy and Benjamin M. Marlin
Advances in Neural Information Processing Systems 22, 2009


A unified framework for high-dimensional analysis of $M$-estimators with decomposable regularizers
Sahand Negahban, Bin Yu, Martin J. Wainwright and Pradeep K. Ravikumar
Advances in Neural Information Processing Systems 22, 2009


An Integer Projected Fixed Point Method for Graph Matching and MAP Inference
Marius Leordeanu, Martial Hebert and Rahul Sukthankar
Advances in Neural Information Processing Systems 22, 2009


Fast Learning from Non-i.i.d. Observations
Ingo Steinwart and Andreas Christmann
Advances in Neural Information Processing Systems 22, 2009


Efficient Recovery of Jointly Sparse Vectors
Liang Sun, Jun Liu, Jianhui Chen and Jieping Ye
Advances in Neural Information Processing Systems 22, 2009


Heterogeneous multitask learning with joint sparsity constraints
Xiaolin Yang, Seyoung Kim and Eric P. Xing
Advances in Neural Information Processing Systems 22, 2009


Dual Averaging Method for Regularized Stochastic Learning and Online Optimization
Lin Xiao
Advances in Neural Information Processing Systems 22, 2009


Speeding up Magnetic Resonance Image Acquisition by Bayesian Multi-Slice Adaptive Compressed Sensing
Matthias Seeger
Advances in Neural Information Processing Systems 22, 2009


Nonlinear directed acyclic structure learning with weakly additive noise models
Arthur Gretton, Peter Spirtes and Robert E. Tillman
Advances in Neural Information Processing Systems 22, 2009


Asymptotic Analysis of MAP Estimation via the Replica Method and Compressed Sensing
Sundeep Rangan, Vivek Goyal and Alyson K. Fletcher
Advances in Neural Information Processing Systems 22, 2009


Randomized Pruning: Efficiently Calculating Expectations in Large Dynamic Programs
Alexandre Bouchard-côté, Slav Petrov and Dan Klein
Advances in Neural Information Processing Systems 22, 2009


Semi-supervised Learning using Sparse Eigenfunction Bases
Kaushik Sinha and Mikhail Belkin
Advances in Neural Information Processing Systems 22, 2009


Entropic Graph Regularization in Non-Parametric Semi-Supervised Classification
Amarnag Subramanya and Jeff A. Bilmes
Advances in Neural Information Processing Systems 22, 2009


Modeling the spacing effect in sequential category learning
Hongjing Lu, Matthew Weiden and Alan L. Yuille
Advances in Neural Information Processing Systems 22, 2009


Fast, smooth and adaptive regression in metric spaces
Samory Kpotufe
Advances in Neural Information Processing Systems 22, 2009


A Smoothed Approximate Linear Program
Vijay Desai, Vivek Farias and Ciamac C. Moallemi
Advances in Neural Information Processing Systems 22, 2009


A Fast, Consistent Kernel Two-Sample Test
Arthur Gretton, Kenji Fukumizu, Za\"ıd Harchaoui and Bharath K. Sriperumbudur
Advances in Neural Information Processing Systems 22, 2009


Information-theoretic lower bounds on the oracle complexity of convex optimization
Alekh Agarwal, Martin J. Wainwright, Peter L. Bartlett and Pradeep K. Ravikumar
Advances in Neural Information Processing Systems 22, 2009


Segmenting Scenes by Matching Image Composites
Bryan Russell, Alyosha Efros, Josef Sivic, Bill Freeman and Andrew Zisserman
Advances in Neural Information Processing Systems 22, 2009


Accelerated Gradient Methods for Stochastic Optimization and Online Learning
Chonghai Hu, Weike Pan and James T. Kwok
Advances in Neural Information Processing Systems 22, 2009


Perceptual Multistability as Markov Chain Monte Carlo Inference
Samuel Gershman, Ed Vul and Joshua B. Tenenbaum
Advances in Neural Information Processing Systems 22, 2009


Adapting to the Shifting Intent of Search Queries
Umar Syed, Aleksandrs Slivkins and Nina Mishra
Advances in Neural Information Processing Systems 22, 2009


Complexity of Decentralized Control: Special Cases
Martin Allen and Shlomo Zilberstein
Advances in Neural Information Processing Systems 22, 2009


Explaining human multiple object tracking as resource-constrained approximate inference in a dynamic probabilistic model
Edward Vul, Michael C. Frank, Joshua B. Tenenbaum and George Alvarez
Advances in Neural Information Processing Systems 22, 2009


On Invariance in Hierarchical Models
Jake Bouvrie, Lorenzo Rosasco and Tomaso Poggio
Advances in Neural Information Processing Systems 22, 2009


Robust Nonparametric Regression with Metric-Space Valued Output
Matthias Hein
Advances in Neural Information Processing Systems 22, 2009


Learning to Hash with Binary Reconstructive Embeddings
Brian Kulis and Trevor Darrell
Advances in Neural Information Processing Systems 22, 2009


Riffled Independence for Ranked Data
Jonathan Huang and Carlos Guestrin
Advances in Neural Information Processing Systems 22, 2009


Sparse Estimation Using General Likelihoods and Non-Factorial Priors
David P. Wipf and Srikantan S. Nagarajan
Advances in Neural Information Processing Systems 22, 2009


Learning with Compressible Priors
Volkan Cevher
Advances in Neural Information Processing Systems 22, 2009


Distribution-Calibrated Hierarchical Classification
Ofer Dekel
Advances in Neural Information Processing Systems 22, 2009


Nonparametric Latent Feature Models for Link Prediction
Kurt Miller, Michael I. Jordan and Thomas L. Griffiths
Advances in Neural Information Processing Systems 22, 2009


Augmenting Feature-driven fMRI Analyses: Semi-supervised learning and resting state activity
Andreas Bartels, Matthew Blaschko and Jacquelyn A. Shelton
Advances in Neural Information Processing Systems 22, 2009


Slow Learners are Fast
Martin Zinkevich, John Langford and Alex J. Smola
Advances in Neural Information Processing Systems 22, 2009


Orthogonal Matching Pursuit From Noisy Random Measurements: A New Analysis
Sundeep Rangan and Alyson K. Fletcher
Advances in Neural Information Processing Systems 22, 2009


Label Selection on Graphs
Andrew Guillory and Jeff A. Bilmes
Advances in Neural Information Processing Systems 22, 2009


Conditional Neural Fields
Jian Peng, Liefeng Bo and Jinbo Xu
Advances in Neural Information Processing Systems 22, 2009


Efficient Match Kernel between Sets of Features for Visual Recognition
Liefeng Bo and Cristian Sminchisescu
Advances in Neural Information Processing Systems 22, 2009


Slow, Decorrelated Features for Pretraining Complex Cell-like Networks
Yoshua Bengio and James S. Bergstra
Advances in Neural Information Processing Systems 22, 2009


Rethinking LDA: Why Priors Matter
Andrew Mccallum, David M. Mimno and Hanna M. Wallach
Advances in Neural Information Processing Systems 22, 2009


A Neural Implementation of the Kalman Filter
Robert Wilson and Leif Finkel
Advances in Neural Information Processing Systems 22, 2009


On the Convergence of the Concave-Convex Procedure
Gert R. Lanckriet and Bharath K. Sriperumbudur
Advances in Neural Information Processing Systems 22, 2009


Training Factor Graphs with Reinforcement Learning for Efficient MAP Inference
Khashayar Rohanimanesh, Sameer Singh, Andrew Mccallum and Michael J. Black
Advances in Neural Information Processing Systems 22, 2009


Construction of Nonparametric Bayesian Models from Parametric Bayes Equations
Peter Orbanz
Advances in Neural Information Processing Systems 22, 2009


A Generalized Natural Actor-Critic Algorithm
Tetsuro Morimura, Eiji Uchibe, Junichiro Yoshimoto and Kenji Doya
Advances in Neural Information Processing Systems 22, 2009


Compressed Least-Squares Regression
Odalric Maillard and Rémi Munos
Advances in Neural Information Processing Systems 22, 2009


Modelling Relational Data using Bayesian Clustered Tensor Factorization
Ilya Sutskever, Joshua B. Tenenbaum and Ruslan Salakhutdinov
Advances in Neural Information Processing Systems 22, 2009


Lattice Regression
Eric Garcia and Maya Gupta
Advances in Neural Information Processing Systems 22, 2009


Kernel Choice and Classifiability for RKHS Embeddings of Probability Distributions
Kenji Fukumizu, Arthur Gretton, Gert R. Lanckriet, Bernhard Schölkopf and Bharath K. Sriperumbudur
Advances in Neural Information Processing Systems 22, 2009


Approximating MAP by Compensating for Structural Relaxations
Arthur Choi and Adnan Darwiche
Advances in Neural Information Processing Systems 22, 2009


Learning models of object structure
Joseph Schlecht and Kobus Barnard
Advances in Neural Information Processing Systems 22, 2009


A Game-Theoretic Approach to Hypergraph Clustering
Samuel R. Bulò and Marcello Pelillo
Advances in Neural Information Processing Systems 22, 2009


Polynomial Semantic Indexing
Bing Bai, Jason Weston, David Grangier, Ronan Collobert, Kunihiko Sadamasa, Yanjun Qi, Corinna Cortes and Mehryar Mohri
Advances in Neural Information Processing Systems 22, 2009


Large Scale Nonparametric Bayesian Inference: Data Parallelisation in the Indian Buffet Process
Finale Doshi-velez, Shakir Mohamed, Zoubin Ghahramani and David A. Knowles
Advances in Neural Information Processing Systems 22, 2009


Efficient and Accurate Lp-Norm Multiple Kernel Learning
Marius Kloft, Ulf Brefeld, Pavel Laskov, Klaus-robert Müller, Alexander Zien and Sören Sonnenburg
Advances in Neural Information Processing Systems 22, 2009


Which graphical models are difficult to learn?
Andrea Montanari and Jose A. Pereira
Advances in Neural Information Processing Systems 22, 2009


Grouped Orthogonal Matching Pursuit for Variable Selection and Prediction
Grzegorz Swirszcz, Naoki Abe and Aurelie C. Lozano
Advances in Neural Information Processing Systems 22, 2009


Group Sparse Coding
Samy Bengio, Fernando Pereira, Yoram Singer and Dennis Strelow
Advances in Neural Information Processing Systems 22, 2009


Matrix Completion from Power-Law Distributed Samples
Raghu Meka, Prateek Jain and Inderjit S. Dhillon
Advances in Neural Information Processing Systems 22, 2009


Particle-based Variational Inference for Continuous Systems
Andrew Frank, Padhraic Smyth and Alexander T. Ihler
Advances in Neural Information Processing Systems 22, 2009


Time-Varying Dynamic Bayesian Networks
Le Song, Mladen Kolar and Eric P. Xing
Advances in Neural Information Processing Systems 22, 2009


FACTORIE: Probabilistic Programming via Imperatively Defined Factor Graphs
Andrew Mccallum, Karl Schultz and Sameer Singh
Advances in Neural Information Processing Systems 22, 2009


Thresholding Procedures for High Dimensional Variable Selection and Statistical Estimation
Shuheng Zhou
Advances in Neural Information Processing Systems 22, 2009


Semi-supervised Regression using Hessian energy with an application to semi-supervised dimensionality reduction
Kwang I. Kim, Florian Steinke and Matthias Hein
Advances in Neural Information Processing Systems 22, 2009


An Online Algorithm for Large Scale Image Similarity Learning
Gal Chechik, Uri Shalit, Varun Sharma and Samy Bengio
Advances in Neural Information Processing Systems 22, 2009


Reconstruction of Sparse Circuits Using Multi-neuronal Excitation (RESCUME)
Tao Hu, Anthony Leonardo and Dmitri B. Chklovskii
Advances in Neural Information Processing Systems 22, 2009


Convex Relaxation of Mixture Regression with Efficient Algorithms
Novi Quadrianto, John Lim, Dale Schuurmans and Tibério S. Caetano
Advances in Neural Information Processing Systems 22, 2009


Strategy Grafting in Extensive Games
Kevin Waugh, Nolan Bard and Michael Bowling
Advances in Neural Information Processing Systems 22, 2009


Maximum likelihood trajectories for continuous-time Markov chains
Theodore J. Perkins
Advances in Neural Information Processing Systems 22, 2009


Learning a Small Mixture of Trees
M. P. Kumar and Daphne Koller
Advances in Neural Information Processing Systems 22, 2009


Replicated Softmax: an Undirected Topic Model
Geoffrey E. Hinton and Ruslan Salakhutdinov
Advances in Neural Information Processing Systems 22, 2009


Asymptotically Optimal Regularization in Smooth Parametric Models
Percy Liang, Guillaume Bouchard, Francis R. Bach and Michael I. Jordan
Advances in Neural Information Processing Systems 22, 2009


Skill Discovery in Continuous Reinforcement Learning Domains using Skill Chaining
George Konidaris and Andre S. Barreto
Advances in Neural Information Processing Systems 22, 2009


Tracking Dynamic Sources of Malicious Activity at Internet Scale
Shobha Venkataraman, Avrim Blum, Dawn Song, Subhabrata Sen and Oliver Spatscheck
Advances in Neural Information Processing Systems 22, 2009


3D Object Recognition with Deep Belief Nets
Vinod Nair and Geoffrey E. Hinton
Advances in Neural Information Processing Systems 22, 2009


Estimating image bases for visual image reconstruction from human brain activity
Yusuke Fujiwara, Yoichi Miyawaki and Yukiyasu Kamitani
Advances in Neural Information Processing Systems 22, 2009


Learning Brain Connectivity of Alzheimer's Disease from Neuroimaging Data
Shuai Huang, Jing Li, Liang Sun, Jun Liu, Teresa Wu, Kewei Chen, Adam Fleisher, Eric Reiman and Jieping Ye
Advances in Neural Information Processing Systems 22, 2009


Fast Graph Laplacian Regularized Kernel Learning via Semidefinite–Quadratic–Linear Programming
Xiao-ming Wu, Anthony M. So, Zhenguo Li and Shuo-yen R. Li
Advances in Neural Information Processing Systems 22, 2009


Sharing Features among Dynamical Systems with Beta Processes
Emily B. Fox, Erik B. Sudderth, Michael I. Jordan and Alan S. Willsky
Advances in Neural Information Processing Systems 22, 2009


Optimal Scoring for Unsupervised Learning
Zhihua Zhang and Guang Dai
Advances in Neural Information Processing Systems 22, 2009


Clustering sequence sets for motif discovery
Jong K. Kim and Seungjin Choi
Advances in Neural Information Processing Systems 22, 2009


Learning in Markov Random Fields using Tempered Transitions
Ruslan Salakhutdinov
Advances in Neural Information Processing Systems 22, 2009


Correlation Coefficients are Insufficient for Analyzing Spike Count Dependencies
Arno Onken, Steffen Grünewälder and Klaus Obermayer
Advances in Neural Information Processing Systems 22, 2009


Canonical Time Warping for Alignment of Human Behavior
Feng Zhou and Fernando Torre
Advances in Neural Information Processing Systems 22, 2009


A Biologically Plausible Model for Rapid Natural Scene Identification
Sennay Ghebreab, Steven Scholte, Victor Lamme and Arnold Smeulders
Advances in Neural Information Processing Systems 22, 2009


Nonparametric Bayesian Models for Unsupervised Event Coreference Resolution
Cosmin Bejan, Matthew Titsworth, Andrew Hickl and Sanda Harabagiu
Advances in Neural Information Processing Systems 22, 2009


Spatial Normalized Gamma Processes
Vinayak Rao and Yee W. Teh
Advances in Neural Information Processing Systems 22, 2009


STDP enables spiking neurons to detect hidden causes of their inputs
Bernhard Nessler, Michael Pfeiffer and Wolfgang Maass
Advances in Neural Information Processing Systems 22, 2009


Predicting the Optimal Spacing of Study: A Multiscale Context Model of Memory
Harold Pashler, Nicholas Cepeda, Robert Lindsey, Ed Vul and Michael C. Mozer
Advances in Neural Information Processing Systems 22, 2009


Adaptive Design Optimization in Experiments with People
Daniel Cavagnaro, Jay Myung and Mark A. Pitt
Advances in Neural Information Processing Systems 22, 2009


Kernel Methods for Deep Learning
Youngmin Cho and Lawrence K. Saul
Advances in Neural Information Processing Systems 22, 2009


Nonlinear Learning using Local Coordinate Coding
Kai Yu, Tong Zhang and Yihong Gong
Advances in Neural Information Processing Systems 22, 2009


Discriminative Network Models of Schizophrenia
Irina Rish, Benjamin Thyreau, Bertrand Thirion, Marion Plaze, Marie-laure Paillere-martinot, Catherine Martelli, Jean-luc Martinot, Jean-baptiste Poline and Guillermo A. Cecchi
Advances in Neural Information Processing Systems 22, 2009


Learning Non-Linear Combinations of Kernels
Corinna Cortes, Mehryar Mohri and Afshin Rostamizadeh
Advances in Neural Information Processing Systems 22, 2009


An LP View of the M-best MAP problem
Menachem Fromer and Amir Globerson
Advances in Neural Information Processing Systems 22, 2009


Variational Gaussian-process factor analysis for modeling spatio-temporal data
Jaakko Luttinen and Alexander T. Ihler
Advances in Neural Information Processing Systems 22, 2009


Localizing Bugs in Program Executions with Graphical Models
Laura Dietz, Valentin Dallmeier, Andreas Zeller and Tobias Scheffer
Advances in Neural Information Processing Systems 22, 2009


A Rate Distortion Approach for Semi-Supervised Conditional Random Fields
Yang Wang, Gholamreza Haffari, Shaojun Wang and Greg Mori
Advances in Neural Information Processing Systems 22, 2009


Efficient Moments-based Permutation Tests
Chunxiao Zhou, Huixia J. Wang and Yongmei M. Wang
Advances in Neural Information Processing Systems 22, 2009


Adaptive Regularization for Transductive Support Vector Machine
Zenglin Xu, Rong Jin, Jianke Zhu, Irwin King, Michael Lyu and Zhirong Yang
Advances in Neural Information Processing Systems 22, 2009


Nonparametric Greedy Algorithms for the Sparse Learning Problem
Han Liu and Xi Chen
Advances in Neural Information Processing Systems 22, 2009


Know Thy Neighbour: A Normative Theory of Synaptic Depression
Jean-pascal Pfister, Peter Dayan and Máté Lengyel
Advances in Neural Information Processing Systems 22, 2009


Learning from Multiple Partially Observed Views - an Application to Multilingual Text Categorization
Massih Amini, Nicolas Usunier and Cyril Goutte
Advances in Neural Information Processing Systems 22, 2009


Noisy Generalized Binary Search
Robert Nowak
Advances in Neural Information Processing Systems 22, 2009


Free energy score space
Alessandro Perina, Marco Cristani, Umberto Castellani, Vittorio Murino and Nebojsa Jojic
Advances in Neural Information Processing Systems 22, 2009


fMRI-Based Inter-Subject Cortical Alignment Using Functional Connectivity
Bryan Conroy, Ben Singer, James Haxby and Peter J. Ramadge
Advances in Neural Information Processing Systems 22, 2009


Heavy-Tailed Symmetric Stochastic Neighbor Embedding
Zhirong Yang, Irwin King, Zenglin Xu and Erkki Oja
Advances in Neural Information Processing Systems 22, 2009


Statistical Models of Linear and Nonlinear Contextual Interactions in Early Visual Processing
Ruben Coen-cagli, Peter Dayan and Odelia Schwartz
Advances in Neural Information Processing Systems 22, 2009


Extending Phase Mechanism to Differential Motion Opponency for Motion Pop-out
Yicong Meng and Bertram E. Shi
Advances in Neural Information Processing Systems 22, 2009


Constructing Topological Maps using Markov Random Fields and Loop-Closure Detection
Roy Anati and Kostas Daniilidis
Advances in Neural Information Processing Systems 22, 2009


Non-stationary continuous dynamic Bayesian networks
Marco Grzegorczyk and Dirk Husmeier
Advances in Neural Information Processing Systems 22, 2009


Bayesian Sparse Factor Models and DAGs Inference and Comparison
Ricardo Henao and Ole Winther
Advances in Neural Information Processing Systems 22, 2009


Differential Use of Implicit Negative Evidence in Generative and Discriminative Language Learning
Anne Hsu and Thomas L. Griffiths
Advances in Neural Information Processing Systems 22, 2009


The Wisdom of Crowds in the Recollection of Order Information
Mark Steyvers, Brent Miller, Pernille Hemmer and Michael D. Lee
Advances in Neural Information Processing Systems 22, 2009


Positive Semidefinite Metric Learning with Boosting
Chunhua Shen, Junae Kim, Lei Wang and Anton Hengel
Advances in Neural Information Processing Systems 22, 2009


Streaming Pointwise Mutual Information
Benjamin V. Durme and Ashwin Lall
Advances in Neural Information Processing Systems 22, 2009


Adaptive Regularization of Weight Vectors
Koby Crammer, Alex Kulesza and Mark Dredze
Advances in Neural Information Processing Systems 22, 2009


Graph-based Consensus Maximization among Multiple Supervised and Unsupervised Models
Jing Gao, Feng Liang, Wei Fan, Yizhou Sun and Jiawei Han
Advances in Neural Information Processing Systems 22, 2009


Anomaly Detection with Score functions based on Nearest Neighbor Graphs
Manqi Zhao and Venkatesh Saligrama
Advances in Neural Information Processing Systems 22, 2009


On the Algorithmics and Applications of a Mixed-norm based Kernel Learning Formulation
Saketha N. Jagarlapudi, Dinesh G, Raman S, Chiranjib Bhattacharyya, Aharon Ben-tal and Ramakrishnan K.r.
Advances in Neural Information Processing Systems 22, 2009


Bayesian Belief Polarization
Alan Jern, Kai-min Chang and Charles Kemp
Advances in Neural Information Processing Systems 22, 2009


Replacing supervised classification learning by Slow Feature Analysis in spiking neural networks
Stefan Klampfl and Wolfgang Maass
Advances in Neural Information Processing Systems 22, 2009


Code-specific policy gradient rules for spiking neurons
Henning Sprekeler, Guillaume Hennequin and Wulfram Gerstner
Advances in Neural Information Processing Systems 22, 2009


Submodularity Cuts and Applications
Yoshinobu Kawahara, Kiyohito Nagano, Koji Tsuda and Jeff A. Bilmes
Advances in Neural Information Processing Systems 22, 2009


Region-based Segmentation and Object Detection
Stephen Gould, Tianshi Gao and Daphne Koller
Advances in Neural Information Processing Systems 22, 2009


Multiple Incremental Decremental Learning of Support Vector Machines
Masayuki Karasuyama and Ichiro Takeuchi
Advances in Neural Information Processing Systems 22, 2009


Hierarchical Mixture of Classification Experts Uncovers Interactions between Brain Regions
Bangpeng Yao, Dirk Walther, Diane Beck and Li Fei-fei
Advances in Neural Information Processing Systems 22, 2009


Bayesian Nonparametric Models on Decomposable Graphs
Francois Caron and Arnaud Doucet
Advances in Neural Information Processing Systems 22, 2009


Factor Modeling for Advertisement Targeting
Ye Chen, Michael Kapralov, John Canny and Dmitry Y. Pavlov
Advances in Neural Information Processing Systems 22, 2009


Parallel Inference for Latent Dirichlet Allocation on Graphics Processing Units
Feng Yan, Ningyi Xu and Yuan Qi
Advances in Neural Information Processing Systems 22, 2009


Sparsistent Learning of Varying-coefficient Models with Structural Changes
Mladen Kolar, Le Song and Eric P. Xing
Advances in Neural Information Processing Systems 22, 2009


Inter-domain Gaussian Processes for Sparse Inference using Inducing Features
Anibal Figueiras-vidal and Miguel Lázaro-gredilla
Advances in Neural Information Processing Systems 22, 2009


Nash Equilibria of Static Prediction Games
Michael Brückner and Tobias Scheffer
Advances in Neural Information Processing Systems 22, 2009


Fast subtree kernels on graphs
Nino Shervashidze and Karsten M. Borgwardt
Advances in Neural Information Processing Systems 22, 2009


The "tree-dependent components" of natural scenes are edge filters
Daniel Zoran and Yair Weiss
Advances in Neural Information Processing Systems 22, 2009


Statistical Consistency of Top-k Ranking
Fen Xia, Tie-yan Liu and Hang Li
Advances in Neural Information Processing Systems 22, 2009


Distribution Matching for Transduction
Novi Quadrianto, James Petterson and Alex J. Smola
Advances in Neural Information Processing Systems 22, 2009


Learning from Neighboring Strokes: Combining Appearance and Context for Multi-Domain Sketch Recognition
Tom Ouyang and Randall Davis
Advances in Neural Information Processing Systems 22, 2009


The Infinite Partially Observable Markov Decision Process
Finale Doshi-velez
Advances in Neural Information Processing Systems 22, 2009


Time-rescaling methods for the estimation and assessment of non-Poisson neural encoding models
Jonathan W. Pillow
Advances in Neural Information Processing Systems 22, 2009


Bootstrapping from Game Tree Search
Joel Veness, David Silver, Alan Blair and William W. Cohen
Advances in Neural Information Processing Systems 22, 2009


The Ordered Residual Kernel for Robust Motion Subspace Clustering
Tat-jun Chin, Hanzi Wang and David Suter
Advances in Neural Information Processing Systems 22, 2009


Ranking Measures and Loss Functions in Learning to Rank
Wei Chen, Tie-yan Liu, Yanyan Lan, Zhi-ming Ma and Hang Li
Advances in Neural Information Processing Systems 22, 2009


Manifold Regularization for SIR with Rate Root-n Convergence
Wei Bian and Dacheng Tao
Advances in Neural Information Processing Systems 22, 2009


Beyond Convexity: Online Submodular Minimization
Elad Hazan and Satyen Kale
Advances in Neural Information Processing Systems 22, 2009


On Stochastic and Worst-case Models for Investing
Elad Hazan and Satyen Kale
Advances in Neural Information Processing Systems 22, 2009


Statistical Analysis of Semi-Supervised Learning: The Limit of Infinite Unlabelled Data
Boaz Nadler, Nathan Srebro and Xueyuan Zhou
Advances in Neural Information Processing Systems 22, 2009


Indian Buffet Processes with Power-law Behavior
Yee W. Teh and Dilan Gorur
Advances in Neural Information Processing Systems 22, 2009


Measuring Invariances in Deep Networks
Ian Goodfellow, Honglak Lee, Quoc V. Le, Andrew Saxe and Andrew Y. Ng
Advances in Neural Information Processing Systems 22, 2009


A Data-Driven Approach to Modeling Choice
Vivek Farias, Srikanth Jagabathula and Devavrat Shah
Advances in Neural Information Processing Systems 22, 2009


From PAC-Bayes Bounds to KL Regularization
Pascal Germain, Alexandre Lacasse, Mario Marchand, Sara Shanian and François Laviolette
Advances in Neural Information Processing Systems 22, 2009


Learning Label Embeddings for Nearest-Neighbor Multi-class Classification with an Application to Speech Recognition
Natasha Singh-miller and Michael Collins
Advances in Neural Information Processing Systems 22, 2009


Semi-Supervised Learning in Gigantic Image Collections
Rob Fergus, Yair Weiss and Antonio Torralba
Advances in Neural Information Processing Systems 22, 2009


Linear-time Algorithms for Pairwise Statistical Problems
Parikshit Ram, Dongryeol Lee, William March and Alexander G. Gray
Advances in Neural Information Processing Systems 22, 2009


Rank-Approximate Nearest Neighbor Search: Retaining Meaning and Speed in High Dimensions
Parikshit Ram, Dongryeol Lee, Hua Ouyang and Alexander G. Gray
Advances in Neural Information Processing Systems 22, 2009


Ensemble Nystrom Method
Sanjiv Kumar, Mehryar Mohri and Ameet Talwalkar
Advances in Neural Information Processing Systems 22, 2009


A Bayesian Model for Simultaneous Image Clustering, Annotation and Object Segmentation
Lan Du, Lu Ren, Lawrence Carin and David B. Dunson
Advances in Neural Information Processing Systems 22, 2009


Online Learning of Assignments
Matthew Streeter, Daniel Golovin and Andreas Krause
Advances in Neural Information Processing Systems 22, 2009


Graph Zeta Function in the Bethe Free Energy and Loopy Belief Propagation
Yusuke Watanabe and Kenji Fukumizu
Advances in Neural Information Processing Systems 22, 2009


An Additive Latent Feature Model for Transparent Object Recognition
Mario Fritz, Gary Bradski, Sergey Karayev, Trevor Darrell and Michael J. Black
Advances in Neural Information Processing Systems 22, 2009


Zero-shot Learning with Semantic Output Codes
Mark Palatucci, Dean Pomerleau, Geoffrey E. Hinton and Tom M. Mitchell
Advances in Neural Information Processing Systems 22, 2009


Improving Existing Fault Recovery Policies
Guy Shani and Christopher Meek
Advances in Neural Information Processing Systems 22, 2009


Directed Regression
Yi-hao Kao, Benjamin V. Roy and Xiang Yan
Advances in Neural Information Processing Systems 22, 2009


Speaker Comparison with Inner Product Discriminant Functions
Zahi Karam, Douglas Sturim and William M. Campbell
Advances in Neural Information Processing Systems 22, 2009


Monte Carlo Sampling for Regret Minimization in Extensive Games
Marc Lanctot, Kevin Waugh, Martin Zinkevich and Michael Bowling
Advances in Neural Information Processing Systems 22, 2009


Bayesian Source Localization with the Multivariate Laplace Prior
Marcel V. Gerven, Botond Cseke, Robert Oostenveld and Tom Heskes
Advances in Neural Information Processing Systems 22, 2009


Matrix Completion from Noisy Entries
Raghunandan Keshavan, Andrea Montanari and Sewoong Oh
Advances in Neural Information Processing Systems 22, 2009


On Learning Rotations
Raman Arora
Advances in Neural Information Processing Systems 22, 2009


Linearly constrained Bayesian matrix factorization for blind source separation
Mikkel Schmidt
Advances in Neural Information Processing Systems 22, 2009


A Bayesian Analysis of Dynamics in Free Recall
Richard Socher, Samuel Gershman, Per Sederberg, Kenneth Norman, Adler J. Perotte and David M. Blei
Advances in Neural Information Processing Systems 22, 2009


Potential-Based Agnostic Boosting
Varun Kanade and Adam Kalai
Advances in Neural Information Processing Systems 22, 2009


Efficient Large-Scale Distributed Training of Conditional Maximum Entropy Models
Ryan Mcdonald, Mehryar Mohri, Nathan Silberman, Dan Walker and Gideon S. Mann
Advances in Neural Information Processing Systems 22, 2009


Learning to Rank by Optimizing NDCG Measure
Hamed Valizadegan, Rong Jin, Ruofei Zhang and Jianchang Mao
Advances in Neural Information Processing Systems 22, 2009


Fast Image Deconvolution using Hyper-Laplacian Priors
Dilip Krishnan and Rob Fergus
Advances in Neural Information Processing Systems 22, 2009


AUC optimization and the two-sample problem
Nicolas Vayatis, Marine Depecker and Stéphan J. Clémençcon
Advances in Neural Information Processing Systems 22, 2009


Learning Bregman Distance Functions and Its Application for Semi-Supervised Clustering
Lei Wu, Rong Jin, Steven C. Hoi, Jianke Zhu and Nenghai Yu
Advances in Neural Information Processing Systems 22, 2009


Toward Provably Correct Feature Selection in Arbitrary Domains
Dimitris Margaritis
Advances in Neural Information Processing Systems 22, 2009


Regularized Distance Metric Learning:Theory and Algorithm
Rong Jin, Shijun Wang and Yang Zhou
Advances in Neural Information Processing Systems 22, 2009


Sparse Metric Learning via Smooth Optimization
Yiming Ying, Kaizhu Huang and Colin Campbell
Advances in Neural Information Processing Systems 22, 2009


Conditional Random Fields with High-Order Features for Sequence Labeling
Nan Ye, Wee S. Lee, Hai L. Chieu and Dan Wu
Advances in Neural Information Processing Systems 22, 2009


Bayesian estimation of orientation preference maps
Sebastian Gerwinn, Leonard White, Matthias Kaschube, Matthias Bethge and Jakob H. Macke
Advances in Neural Information Processing Systems 22, 2009


Data-driven calibration of linear estimators with minimal penalties
Sylvain Arlot and Francis R. Bach
Advances in Neural Information Processing Systems 22, 2009


A Gaussian Tree Approximation for Integer Least-Squares
Jacob Goldberger and Amir Leshem
Advances in Neural Information Processing Systems 22, 2009


Sensitivity analysis in HMMs with application to likelihood maximization
Pierre-arnaud Coquelin, Romain Deguest and Rémi Munos
Advances in Neural Information Processing Systems 22, 2009


Modeling Social Annotation Data with Content Relevance using a Topic Model
Tomoharu Iwata, Takeshi Yamada and Naonori Ueda
Advances in Neural Information Processing Systems 22, 2009


Human Rademacher Complexity
Xiaojin Zhu, Bryan R. Gibson and Timothy T. Rogers
Advances in Neural Information Processing Systems 22, 2009


Efficient Bregman Range Search
Lawrence Cayton
Advances in Neural Information Processing Systems 22, 2009


Multi-Step Dyna Planning for Policy Evaluation and Control
Hengshuai Yao, Shalabh Bhatnagar, Dongcui Diao, Richard S. Sutton and Csaba Szepesvári
Advances in Neural Information Processing Systems 22, 2009


Neurometric function analysis of population codes
Philipp Berens, Sebastian Gerwinn, Alexander Ecker and Matthias Bethge
Advances in Neural Information Processing Systems 22, 2009


Efficient Learning using Forward-Backward Splitting
Yoram Singer and John C. Duchi
Advances in Neural Information Processing Systems 22, 2009


A joint maximum-entropy model for binary neural population patterns and continuous signals
Sebastian Gerwinn, Philipp Berens and Matthias Bethge
Advances in Neural Information Processing Systems 22, 2009


Optimal context separation of spiking haptic signals by second-order somatosensory neurons
Romain Brasselet, Roland Johansson and Angelo Arleo
Advances in Neural Information Processing Systems 22, 2009


Gaussian process regression with Student-t likelihood
Jarno Vanhatalo, Pasi Jylänki and Aki Vehtari
Advances in Neural Information Processing Systems 22, 2009


Hierarchical Modeling of Local Image Features through $L_p$-Nested Symmetric Distributions
Matthias Bethge, Eero P. Simoncelli and Fabian H. Sinz
Advances in Neural Information Processing Systems 22, 2009


Decoupling Sparsity and Smoothness in the Discrete Hierarchical Dirichlet Process
Chong Wang and David M. Blei
Advances in Neural Information Processing Systems 22, 2009


Variational Inference for the Nested Chinese Restaurant Process
Chong Wang and David M. Blei
Advances in Neural Information Processing Systems 22, 2009


Functional network reorganization in motor cortex can be explained by reward-modulated Hebbian learning
Steven Chase, Andrew Schwartz, Wolfgang Maass and Robert A. Legenstein
Advances in Neural Information Processing Systems 22, 2009


Sufficient Conditions for Agnostic Active Learnable
Liwei Wang
Advances in Neural Information Processing Systems 22, 2009


Probabilistic Relational PCA
Wu-jun Li, Dit-yan Yeung and Zhihua Zhang
Advances in Neural Information Processing Systems 22, 2009


Exponential Family Graph Matching and Ranking
James Petterson, Jin Yu, Julian J. Mcauley and Tibério S. Caetano
Advances in Neural Information Processing Systems 22, 2009


Non-Parametric Bayesian Dictionary Learning for Sparse Image Representations
Mingyuan Zhou, Haojun Chen, Lu Ren, Guillermo Sapiro, Lawrence Carin and John W. Paisley
Advances in Neural Information Processing Systems 22, 2009


Manifold Embeddings for Model-Based Reinforcement Learning under Partial Observability
Keith Bush and Joelle Pineau
Advances in Neural Information Processing Systems 22, 2009


Local Rules for Global MAP: When Do They Work ?
Kyomin Jung, Pushmeet Kohli and Devavrat Shah
Advances in Neural Information Processing Systems 22, 2009


Breaking Boundaries Between Induction Time and Diagnosis Time Active Information Acquisition
Ashish Kapoor and Eric Horvitz
Advances in Neural Information Processing Systems 22, 2009


Abstraction and Relational learning
Charles Kemp and Alan Jern
Advances in Neural Information Processing Systems 22, 2009


Individuation, Identification and Object Discovery
Charles Kemp, Alan Jern and Fei Xu
Advances in Neural Information Processing Systems 22, 2009


Quantification and the language of thought
Charles Kemp
Advances in Neural Information Processing Systems 22, 2009


Multi-Label Prediction via Compressed Sensing
John Langford, Tong Zhang, Daniel J. Hsu and Sham M. Kakade
Advances in Neural Information Processing Systems 22, 2009


Nonparametric Bayesian Texture Learning and Synthesis
Long Zhu, Yuanahao Chen, Bill Freeman and Antonio Torralba
Advances in Neural Information Processing Systems 22, 2009


Boosting with Spatial Regularization
Yongxin Xi, Uri Hasson, Peter J. Ramadge and Zhen J. Xiang
Advances in Neural Information Processing Systems 22, 2009


Locality-sensitive binary codes from shift-invariant kernels
Maxim Raginsky and Svetlana Lazebnik
Advances in Neural Information Processing Systems 22, 2009


No evidence for active sparsification in the visual cortex
Pietro Berkes, Ben White and Jozsef Fiser
Advances in Neural Information Processing Systems 22, 2009


Evaluating multi-class learning strategies in a generative hierarchical framework for object detection
Sanja Fidler, Marko Boben and Ales Leonardis
Advances in Neural Information Processing Systems 22, 2009


DUOL: A Double Updating Approach for Online Learning
Peilin Zhao, Steven C. Hoi and Rong Jin
Advances in Neural Information Processing Systems 22, 2009


Analysis of SVM with Indefinite Kernels
Yiming Ying, Colin Campbell and Mark Girolami
Advances in Neural Information Processing Systems 22, 2009


$L_1$-Penalized Robust Estimation for a Class of Inverse Problems Arising in Multiview Geometry
Arnak Dalalyan and Renaud Keriven
Advances in Neural Information Processing Systems 22, 2009


Robust Value Function Approximation Using Bilinear Programming
Marek Petrik and Shlomo Zilberstein
Advances in Neural Information Processing Systems 22, 2009


Reading Tea Leaves: How Humans Interpret Topic Models
Jonathan Chang, Sean Gerrish, Chong Wang, Jordan L. Boyd-graber and David M. Blei
Advances in Neural Information Processing Systems 22, 2009


Robust Principal Component Analysis: Exact Recovery of Corrupted Low-Rank Matrices via Convex Optimization
John Wright, Arvind Ganesh, Shankar Rao, Yigang Peng and Yi Ma
Advances in Neural Information Processing Systems 22, 2009


Whose Vote Should Count More: Optimal Integration of Labels from Labelers of Unknown Expertise
Jacob Whitehill, Ting-fan Wu, Jacob Bergsma, Javier R. Movellan and Paul L. Ruvolo
Advances in Neural Information Processing Systems 22, 2009


Kernels and learning curves for Gaussian process regression on random graphs
Peter Sollich, Matthew Urry and Camille Coti
Advances in Neural Information Processing Systems 22, 2009


Structured output regression for detection with partial truncation
Andrea Vedaldi and Andrew Zisserman
Advances in Neural Information Processing Systems 22, 2009


Beyond Categories: The Visual Memex Model for Reasoning About Object Relationships
Tomasz Malisiewicz and Alyosha Efros
Advances in Neural Information Processing Systems 22, 2009


Maximin affinity learning of image segmentation
Kevin Briggman, Winfried Denk, Sebastian Seung, Moritz N. Helmstaedter and Srinivas C. Turaga
Advances in Neural Information Processing Systems 22, 2009


Generalization Errors and Learning Curves for Regression with Multi-task Gaussian Processes
Kian M. Chai
Advances in Neural Information Processing Systems 22, 2009


A General Projection Property for Distribution Families
Yao-liang Yu, Yuxi Li, Dale Schuurmans and Csaba Szepesvári
Advances in Neural Information Processing Systems 22, 2009


Dirichlet-Bernoulli Alignment: A Generative Model for Multi-Class Multi-Label Multi-Instance Corpora
Shuang-hong Yang, Hongyuan Zha and Bao-gang Hu
Advances in Neural Information Processing Systems 22, 2009


Occlusive Components Analysis
Jörg Lücke, Richard Turner, Maneesh Sahani and Marc Henniges
Advances in Neural Information Processing Systems 22, 2009


A Stochastic approximation method for inference in probabilistic graphical models
Peter Carbonetto, Matthew King and Firas Hamze
Advances in Neural Information Processing Systems 22, 2009


Optimizing Multi-Class Spatio-Spectral Filters via Bayes Error Estimation for EEG Classification
Wenming Zheng and Zhouchen Lin
Advances in Neural Information Processing Systems 22, 2009


Noise Characterization, Modeling, and Reduction for In Vivo Neural Recording
Zhi Yang, Qi Zhao, Edward Keefer and Wentai Liu
Advances in Neural Information Processing Systems 22, 2009


Sparse and Locally Constant Gaussian Graphical Models
Jean Honorio, Dimitris Samaras, Nikos Paragios, Rita Goldstein and Luis E. Ortiz
Advances in Neural Information Processing Systems 22, 2009


Unsupervised Feature Selection for the $k$-means Clustering Problem
Christos Boutsidis, Petros Drineas and Michael W. Mahoney
Advances in Neural Information Processing Systems 22, 2009


Discrete MDL Predicts in Total Variation
Marcus Hutter
Advances in Neural Information Processing Systems 22, 2009


Unsupervised Detection of Regions of Interest Using Iterative Link Analysis
Gunhee Kim and Antonio Torralba
Advances in Neural Information Processing Systems 22, 2009


Reversible Jump MCMC for Non-Negative Matrix Factorization
Mingjun Zhong and Mark Girolami
Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics (AISTATS-09), 2009


Coherence Functions for Multicategory Margin-based Classification Methods
Zhihua Zhang, Michael I. Jordan, Wu-jun Li and Dit-yan Yeung
Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics (AISTATS-09), 2009


Latent Variable Models for Dimensionality Reduction
Zhihua Zhang and Michael I. Jordan
Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics (AISTATS-09), 2009


Active Sensing
Shipeng Yu, Balaji Krishnapuram, Rómer Rosales and R. B. Rao
Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics (AISTATS-09), 2009


Dual Temporal Difference Learning
Min Yang, Yuxi Li and Dale Schuurmans
Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics (AISTATS-09), 2009


Tree-Based Inference for Dirichlet Process Mixtures
Yang Xu, Katherine A. Heller and Zoubin Ghahramani
Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics (AISTATS-09), 2009


Speed and Sparsity of Regularized Boosting
Yongxin T. Xi, Zhen J. Xiang, Peter J. Ramadge and Robert E. Schapire
Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics (AISTATS-09), 2009


A Hierarchical Nonparametric Bayesian Approach to Statistical Language Model Domain Adaptation
Frank Wood and Yee W. Teh
Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics (AISTATS-09), 2009


Non-Negative Semi-Supervised Learning
Changhu Wang, Shuicheng Yan, Lei Zhang and Hongjiang Zhang
Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics (AISTATS-09), 2009


Markov Topic Models
Chong Wang, Bo Thiesson, Christopher Meek and David M. Blei
Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics (AISTATS-09), 2009


Large-Margin Structured Prediction via Linear Programming
Zhuoran Wang and John Shawe-taylor
Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics (AISTATS-09), 2009


An Information Geometry Approach for Distance Metric Learning
Shijun Wang and Rong Jin
Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics (AISTATS-09), 2009


Variational Learning of Inducing Variables in Sparse Gaussian Processes
Michalis K. Titsias
Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics (AISTATS-09), 2009


Variable Metric Stochastic Approximation Theory
Peter Sunehag, Jochen Trumpf, Nicol N. Schraudolph and S.v.n. Vishwanathan
Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics (AISTATS-09), 2009


The Block Diagonal Infinite Hidden Markov Model
Thomas S. Stepleton, Zoubin Ghahramani, Geoffrey J. Gordon and Tai S. Lee
Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics (AISTATS-09), 2009


Tree Block Coordinate Descent for MAP in Graphical Models
David Sontag and Tommi Jaakkola
Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics (AISTATS-09), 2009


Tractable Bayesian Inference of Time-Series Dependence Structure
Michael Siracusa and John Iii
Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics (AISTATS-09), 2009


Factorial Mixture of Gaussians and the Marginal Independence Model
Ricardo Silva and Zoubin Ghahramani
Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics (AISTATS-09), 2009


MCMC Methods for Bayesian Mixtures of Copulas
Ricardo Silva and Robert B. Gramacy
Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics (AISTATS-09), 2009


Locally Minimax Optimal Predictive Modeling with Bayesian Networks
Tomi Silander, Teemu Roos and Petri Myllymäki
Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics (AISTATS-09), 2009


Hash Kernels
Qinfeng Shi, James Petterson, Gideon Dror, John Langford, Alexander L. Strehl, Alex J. Smola and S.v.n. Vishwanathan
Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics (AISTATS-09), 2009


Efficient graphlet kernels for large graph comparison
Nino Shervashidze, Tobias Petri, Kurt Mehlhorn, Karsten M. Borgwardt and S.v.n. Vishwanathan
Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics (AISTATS-09), 2009


PAC-Bayes Analysis Of Maximum Entropy Classification
John Shawe-taylor and David R. Hardoon
Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics (AISTATS-09), 2009


Learning Thin Junction Trees via Graph Cuts
Dafna Shahaf and Carlos Guestrin
Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics (AISTATS-09), 2009


PAC-Bayesian Generalization Bound for Density Estimation with Application to Co-clustering
Yevgeny Seldin and Naftali Tishby
Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics (AISTATS-09), 2009


Novelty detection: Unlabeled data definitely help
Clayton Scott and Gilles Blanchard
Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics (AISTATS-09), 2009


Optimizing Costly Functions with Simple Constraints: A Limited-Memory Projected Quasi-Newton Algorithm
Mark W. Schmidt, Ewout Berg, Michael P. Friedlander and Kevin P. Murphy
Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics (AISTATS-09), 2009


Deep Boltzmann Machines
Ruslan Salakhutdinov and Geoffrey E. Hinton
Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics (AISTATS-09), 2009


Sequential Learning of Classifiers for Structured Prediction Problems
Dan Roth, Kevin Small and Ivan Titov
Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics (AISTATS-09), 2009


Learning the Switching Rate by Discretising Bernoulli Sources Online
Steven D. Rooij and Tim V. Erven
Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics (AISTATS-09), 2009


Inverse Optimal Heuristic Control for Imitation Learning
Nathan D. Ratliff, Brian D. Ziebart, Kevin Peterson, J. A. Bagnell, Martial Hebert, Anind K. Dey and Siddhartha S. Srinivasa
Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics (AISTATS-09), 2009


Chromatic PAC-Bayes Bounds for Non-IID Data
Liva Ralaivola, Marie Szafranski and Guillaume Stempfel
Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics (AISTATS-09), 2009


Spanning Tree Approximations for Conditional Random Fields
Patrick Pletscher, Cheng S. Ong and Joachim M. Buhmann
Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics (AISTATS-09), 2009


Exact and Approximate Sampling by Systematic Stochastic Search
Vikash K. Mansinghka, Daniel M. Roy, Eric Jonas and Joshua B. Tenenbaum
Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics (AISTATS-09), 2009


Tractable Search for Learning Exponential Models of Rankings
Bhushan Mandhani and Marina Meila
Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics (AISTATS-09), 2009


Learning a Parametric Embedding by Preserving Local Structure
Laurens Maaten
Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics (AISTATS-09), 2009


Estimation Consistency of the Group Lasso and its Applications
Han Liu and Jian Zhang
Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics (AISTATS-09), 2009


A kernel method for unsupervised structured network inference
Christoph Lippert, Oliver Stegle, Zoubin Ghahramani and Karsten M. Borgwardt
Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics (AISTATS-09), 2009


Learning Sparse Markov Network Structure via Ensemble-of-Trees Models
Yuanqing Lin, Shenghuo Zhu, Daniel D. Lee and Ben Taskar
Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics (AISTATS-09), 2009


Latent Wishart Processes for Relational Kernel Learning
Wu-jun Li, Zhihua Zhang and Dit-yan Yeung
Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics (AISTATS-09), 2009


Tighter and Convex Maximum Margin Clustering
Yu-feng Li, Ivor W. Tsang, James T. Kwok and Zhi-hua Zhou
Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics (AISTATS-09), 2009


Learning Exercise Policies for American Options
Yuxi Li, Csaba Szepesvári and Dale Schuurmans
Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics (AISTATS-09), 2009


Kernel Learning by Unconstrained Optimization
Fuxin Li, Yun-shan Fu, Yu-hong Dai, Cristian Sminchisescu and Jue Wang
Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics (AISTATS-09), 2009


Group Nonnegative Matrix Factorization for EEG Classification
Hyekyoung Lee and Seungjin Choi
Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics (AISTATS-09), 2009


Deep Learning using Robust Interdependent Codes
Hugo Larochelle, Dumitru Erhan and Pascal Vincent
Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics (AISTATS-09), 2009


Sampling Techniques for the Nystrom Method
Sanjiv Kumar, Mehryar Mohri and Ameet Talwalkar
Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics (AISTATS-09), 2009


Convex Perturbations for Scalable Semidefinite Programming
Brian Kulis, Suvrit Sra and Inderjit S. Dhillon
Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics (AISTATS-09), 2009


Lanczos Approximations for the Speedup of Kernel Partial Least Squares Regression
Nicole Krämer, Masashi Sugiyama and Mikio L. Braun
Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics (AISTATS-09), 2009


Covariance Operator Based Dimensionality Reduction with Extension to Semi-Supervised Settings
Minyoung Kim and Vladimir Pavlovic
Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics (AISTATS-09), 2009


Sleeping Experts and Bandits with Stochastic Action Availability and Adversarial Rewards
Varun Kanade, H. B. Mcmahan and Brent Bryan
Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics (AISTATS-09), 2009


Data Biased Robust Counter Strategies
Michael Johanson and Michael H. Bowling
Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics (AISTATS-09), 2009


Relative Novelty Detection
Le Song, Choon H. Teo and Alex J. Smola
Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics (AISTATS-09), 2009


Particle Belief Propagation
Alexander T. Ihler and David A. Mcallester
Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics (AISTATS-09), 2009


Maximum Entropy Density Estimation with Incomplete Presence-Only Data
Bert C. Huang and Ansaf Salleb-aouissi
Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics (AISTATS-09), 2009


Exploiting Probabilistic Independence for Permutations
Jonathan Huang, Carlos Guestrin, Xiaoye Jiang and Leonidas J. Guibas
Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics (AISTATS-09), 2009


An Expectation Maximization Algorithm for Continuous Markov Decision Processes with Arbitrary Reward
Matthew D. Hoffman, Nando D. Freitas, Arnaud Doucet and Jan R. Peters
Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics (AISTATS-09), 2009


Infinite Hierarchical Hidden Markov Models
Katherine A. Heller, Yee W. Teh and Dilan Görür
Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics (AISTATS-09), 2009


Distilled sensing: selective sampling for sparse signal recovery
Jarvis Haupt, Rui Castro and Robert Nowak
Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics (AISTATS-09), 2009


Network Completion and Survey Sampling
Steve Hanneke and Eric P. Xing
Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics (AISTATS-09), 2009


Active Learning as Non-Convex Optimization
Andrew Guillory, Erick Chastain and Jeff Bilmes
Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics (AISTATS-09), 2009


Visualization Databases for the Analysis of Large Complex Datasets
Saptarshi Guha, Paul Kidwell, Ryan Hafen and William S. Cleveland
Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics (AISTATS-09), 2009


Sparse Probabilistic Principal Component Analysis
Yue Guan and Jennifer G. Dy
Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics (AISTATS-09), 2009


Residual Splash for Optimally Parallelizing Belief Propagation
Joseph Gonzalez, Yucheng Low and Carlos Guestrin
Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics (AISTATS-09), 2009


Multi-Manifold Semi-Supervised Learning
Andrew B. Goldberg, Xiaojin Zhu, Aarti Singh, Zhiting Xu and Robert Nowak
Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics (AISTATS-09), 2009


Semi-Supervised Affinity Propagation with Instance-Level Constraints
Inmar E. Givoni and Brendan J. Frey
Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics (AISTATS-09), 2009


The Difficulty of Training Deep Architectures and the Effect of Unsupervised Pre-Training
Dumitru Erhan, Pierre-antoine Manzagol, Yoshua Bengio, Samy Bengio and Pascal Vincent
Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics (AISTATS-09), 2009


Choosing a Variable to Clamp
Frederik Eaton and Zoubin Ghahramani
Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics (AISTATS-09), 2009


Variational Inference for the Indian Buffet Process
Finale Doshi, Kurt Miller, Jurgen V. Gael and Yee W. Teh
Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics (AISTATS-09), 2009


Statistical and Computational Tradeoffs in Stochastic Composite Likelihood
Joshua V. Dillon and Guy Lebanon
Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics (AISTATS-09), 2009


Matching Pursuit Kernel Fisher Discriminant Analysis
Tom Diethe, Zakria Hussain, David R. Hardoon and John Shawe-taylor
Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics (AISTATS-09), 2009


Gaussian Margin Machines
Koby Crammer, Mehryar Mohri and Fernando Pereira
Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics (AISTATS-09), 2009


On Partitioning Rules for Bipartite Ranking
Stéphan Clémençcon and Nicolas Vayatis
Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics (AISTATS-09), 2009


Probabilistic Models for Incomplete Multi-dimensional Arrays
Wei Chu and Zoubin Ghahramani
Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics (AISTATS-09), 2009


Relational Topic Models for Document Networks
Jonathan Chang and David M. Blei
Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics (AISTATS-09), 2009


Handling Sparsity via the Horseshoe
Carlos M. Carvalho, Nicholas G. Polson and James G. Scott
Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics (AISTATS-09), 2009


Online Inference of Topics with Latent Dirichlet Allocation
Kevin R. Canini, Lei Shi and Thomas L. Griffiths
Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics (AISTATS-09), 2009


Structure Identification by Optimized Interventions
Alberto G. Busetto and Joachim M. Buhmann
Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics (AISTATS-09), 2009


A New Perspective for Information Theoretic Feature Selection
Gavin Brown
Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics (AISTATS-09), 2009


Estimating Tree-Structured Covariance Matrices via Mixed-Integer Programming
Héctor C. Bravo, Stephen J. Wright, Kevin Eng, Sunduz Keles and Grace Wahba
Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics (AISTATS-09), 2009


Supervised Spectral Latent Variable Models
Liefeng Bo and Cristian Sminchisescu
Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics (AISTATS-09), 2009


Learning Low Density Separators
Shai Ben-david, Tyler Lu, Dávid Pál and Miroslava Sotáková
Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics (AISTATS-09), 2009


Variational Bridge Regression
Artin Armagan
Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics (AISTATS-09), 2009


Latent Force Models
Mauricio A. Álvarez, David Luengo and Neil D. Lawrence
Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics (AISTATS-09), 2009


Clusterability: A Theoretical Study
Margareta Ackerman and Shai Ben-david
Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics (AISTATS-09), 2009