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All publications by Daniel J. Hsu
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Mixing Time Estimation in Reversible Markov Chains from a Single Sample Path
Daniel J. Hsu, Aryeh Kontorovich and Csaba Szepesvari
Advances in Neural Information Processing Systems 28, 2015


Efficient and Parsimonious Agnostic Active Learning
Tzu-kuo Huang, Alekh Agarwal, Daniel J. Hsu, John Langford and Robert E. Schapire
Advances in Neural Information Processing Systems 28, 2015


Scalable Non-linear Learning with Adaptive Polynomial Expansions
Alekh Agarwal, Alina Beygelzimer, Daniel J. Hsu, John Langford and Matus J. Telgarsky
Advances in Neural Information Processing Systems 27, 2014


The Large Margin Mechanism for Differentially Private Maximization
Kamalika Chaudhuri, Daniel J. Hsu and Shuang Song
Advances in Neural Information Processing Systems 27, 2014


Learning Linear Bayesian Networks with Latent Variables
Animashree Anandkumar, Adel Javanmard, Daniel J. Hsu and Sham M. Kakade
Proceedings of the 30th International Conference on Machine Learning (ICML-13), 2013


Identifiability and Unmixing of Latent Parse Trees
Percy Liang, Daniel J. Hsu and Sham M. Kakade
Advances in Neural Information Processing Systems 25, 2012


Learning Mixtures of Tree Graphical Models
Anima Anandkumar, Furong Huang, Daniel J. Hsu and Sham M. Kakade
Advances in Neural Information Processing Systems 25, 2012


A Spectral Algorithm for Latent Dirichlet Allocation
Anima Anandkumar, Yi-kai Liu, Daniel J. Hsu, Dean P. Foster and Sham M. Kakade
Advances in Neural Information Processing Systems 25, 2012


Monte Carlo Bayesian Reinforcement Learning
Yi Wang, Kok S. Won, Wee S. Lee and Daniel J. Hsu
Proceedings of the 29th International Conference on Machine Learning (ICML-12), 2012


Convergence Rates for Differentially Private Statistical Estimation
Kamalika Chaudhuri and Daniel J. Hsu
Proceedings of the 29th International Conference on Machine Learning (ICML-12), 2012


Spectral Methods for Learning Multivariate Latent Tree Structure
Animashree Anandkumar, Kamalika Chaudhuri, Daniel J. Hsu, Sham M. Kakade, Le Song and Tong Zhang
Advances in Neural Information Processing Systems 24, 2011


Monte Carlo Value Iteration with Macro-Actions
Zhan Lim, Lee Sun and Daniel J. Hsu
Advances in Neural Information Processing Systems 24, 2011


Stochastic convex optimization with bandit feedback
Alekh Agarwal, Dean P. Foster, Daniel J. Hsu, Sham M. Kakade and Alexander Rakhlin
Advances in Neural Information Processing Systems 24, 2011


Agnostic Active Learning Without Constraints
Alina Beygelzimer, John Langford, Zhang Tong and Daniel J. Hsu
Advances in Neural Information Processing Systems 23, 2010


A Parameter-free Hedging Algorithm
Kamalika Chaudhuri, Yoav Freund and Daniel J. Hsu
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


Hierarchical sampling for active learning
Sanjoy Dasgupta and Daniel J. Hsu
Proceedings of the 25th International Conference on Machine Learning (ICML-08), 2008


Bayesian Agglomerative Clustering with Coalescents
Yee W. Teh, Daniel J. Hsu and Hal Daume
Advances in Neural Information Processing Systems 20, 2007


What makes some POMDP problems easy to approximate?
Wee S. Lee, Nan Rong and Daniel J. Hsu
Advances in Neural Information Processing Systems 20, 2007


A general agnostic active learning algorithm
Sanjoy Dasgupta, Claire Monteleoni and Daniel J. Hsu
Advances in Neural Information Processing Systems 20, 2007


Field-Programmable Learning Arrays
Seth Bridges, Miguel Figueroa, Chris Diorio and Daniel J. Hsu
Advances in Neural Information Processing Systems 15, 2002


Adaptive Quantization and Density Estimation in Silicon
Seth Bridges, Miguel Figueroa, Chris Diorio and Daniel J. Hsu
Advances in Neural Information Processing Systems 15, 2002