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All publications by Max Welling
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Markov Chain Monte Carlo and Variational Inference: Bridging the Gap
Tim Salimans, Diederik Kingma and Max Welling
Proceedings of the 32nd International Conference on Machine Learning (ICML-15), 2015


Harmonic Exponential Families on Manifolds
Taco Cohen and Max Welling
Proceedings of the 32nd International Conference on Machine Learning (ICML-15), 2015


Bayesian dark knowledge
Anoop K. Balan, Vivek Rathod, Kevin P. Murphy and Max Welling
Advances in Neural Information Processing Systems 28, 2015


Optimization Monte Carlo: Efficient and Embarrassingly Parallel Likelihood-Free Inference
Ted Meeds and Max Welling
Advances in Neural Information Processing Systems 28, 2015


Efficient Gradient-Based Inference through Transformations between Bayes Nets and Neural Nets
Diederik Kingma and Max Welling
Proceedings of the 31st International Conference on Machine Learning (ICML-14), 2014


Learning the Irreducible Representations of Commutative Lie Groups
Taco Cohen and Max Welling
Proceedings of the 31st International Conference on Machine Learning (ICML-14), 2014


Distributed Stochastic Gradient MCMC
Sungjin Ahn, Babak Shahbaba and Max Welling
Proceedings of the 31st International Conference on Machine Learning (ICML-14), 2014


Austerity in MCMC Land: Cutting the Metropolis-Hastings Budget
Anoop Korattikara, Yutian Chen and Max Welling
Proceedings of the 31st International Conference on Machine Learning (ICML-14), 2014


Semi-supervised Learning with Deep Generative Models
Diederik P. Kingma, Shakir Mohamed, Danilo J. Rezende and Max Welling
Advances in Neural Information Processing Systems 27, 2014


The Time-Marginalized Coalescent Prior for Hierarchical Clustering
Levi Boyles and Max Welling
Advances in Neural Information Processing Systems 25, 2012


Bayesian Posterior Sampling via Stochastic Gradient Fisher Scoring
Sungjin Ahn, Anoop Korattikara and Max Welling
Proceedings of the 29th International Conference on Machine Learning (ICML-12), 2012


Scalable Inference on Kingman's Coalescent using Pair Similarity
Dilan Görür, Levi Boyles and Max Welling
Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics (AISTATS-12), 2012


Statistical Tests for Optimization Efficiency
Levi Boyles, Anoop Korattikara, Deva Ramanan and Max Welling
Advances in Neural Information Processing Systems 24, 2011


Bayesian Learning via Stochastic Gradient Langevin Dynamics
Max Welling and Yee W. Teh
Proceedings of the 28th International Conference on Machine Learning (ICML-11), 2011


Hidden-Unit Conditional Random Fields
Laurens Maaten, Max Welling and Lawrence K. Saul
Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics (AISTATS-11), 2011


Statistical Optimization of Non-Negative Matrix Factorization
Anoop K. Balan, Levi Boyles, Max Welling, Jingu Kim and Haesun Park
Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics (AISTATS-11), 2011


Dynamical Products of Experts for Modeling Financial Time Series
Yutian Chen and Max Welling
Proceedings of the 27th International Conference on Machine Learning (ICML-10), 2010


On Herding and the Perceptron Cycling Theorem
Andrew Gelfand, Yutian Chen, Laurens Maaten and Max Welling
Advances in Neural Information Processing Systems 23, 2010


Parametric Herding
Yutian Chen and Max Welling
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS-10), 2010


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


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


Memory bounded inference in topic models
Max Welling, Pietro Perona and Ryan G. Gomes
Proceedings of the 25th International Conference on Machine Learning (ICML-08), 2008


Asynchronous Distributed Learning of Topic Models
Padhraic Smyth, Max Welling and Arthur U. Asuncion
Advances in Neural Information Processing Systems 21, 2008


Collapsed Variational Inference for HDP
Yee W. Teh, Kenichi Kurihara and Max Welling
Advances in Neural Information Processing Systems 20, 2007


Distributed Inference for Latent Dirichlet Allocation
David Newman, Padhraic Smyth, Max Welling and Arthur U. Asuncion
Advances in Neural Information Processing Systems 20, 2007


Infinite State Bayes-Nets for Structured Domains
Max Welling, Ian Porteous and Evgeniy Bart
Advances in Neural Information Processing Systems 20, 2007


Generalized Darting Monte Carlo
Cristian Sminchisescu and Max Welling
Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics (AISTATS-07), 2007


The rate adapting poisson model for information retrieval and object recognition
Peter V. Gehler, Alex Holub and Max Welling
Proceedings of the 23th International Conference on Machine Learning (ICML-06), 2006


Bayesian Model Scoring in Markov Random Fields
Sridevi Parise and Max Welling
Advances in Neural Information Processing Systems 19, 2006


A Collapsed Variational Bayesian Inference Algorithm for Latent Dirichlet Allocation
Yee W. Teh, David Newman and Max Welling
Advances in Neural Information Processing Systems 19, 2006


Accelerated Variational Dirichlet Process Mixtures
Kenichi Kurihara, Max Welling and Nikos A. Vlassis
Advances in Neural Information Processing Systems 19, 2006


Products of ``Edge-perts
Max Welling and Peter V. Gehler
Advances in Neural Information Processing Systems 18, 2005


Approximate inference by Markov chains on union spaces
Max Welling, Michal Rosen-zvi and Yee W. Teh
Proceedings of the 21st International Conference on Machine Learning (ICML-04), 2004


Exponential Family Harmoniums with an Application to Information Retrieval
Max Welling, Michal Rosen-zvi and Geoffrey E. Hinton
Advances in Neural Information Processing Systems 17, 2004


Energy-Based Models for Sparse Overcomplete Representations
Yee W. Teh, Max Welling, Simon Osindero and Geoffrey E. Hinton
Journal of Machine Learning Research, 2003


Wormholes Improve Contrastive Divergence
Max Welling, Andriy Mnih and Geoffrey E. Hinton
Advances in Neural Information Processing Systems 16, 2003


Linear Response for Approximate Inference
Max Welling and Yee W. Teh
Advances in Neural Information Processing Systems 16, 2003


Extreme Components Analysis
Max Welling, Christopher Williams and Felix V. Agakov
Advances in Neural Information Processing Systems 16, 2003


Learning Sparse Topographic Representations with Products of Student-t Distributions
Max Welling, Simon Osindero and Geoffrey E. Hinton
Advances in Neural Information Processing Systems 15, 2002


Self Supervised Boosting
Max Welling, Richard S. Zemel and Geoffrey E. Hinton
Advances in Neural Information Processing Systems 15, 2002


The Unified Propagation and Scaling Algorithm
Yee W. Teh and Max Welling
Advances in Neural Information Processing Systems 14, 2001