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While deep learning has been revolutionary for machine learning, most modern deep learning models cannot represent their uncertainty nor take advantage of the well studied tools of probability theory. This has started to change following recent developments of tools and techniques combining Bayesian approaches with deep learning. The intersection of the two fields has received great interest from the community over the past few years, with the introduction of new deep learning models that take advantage of Bayesian techniques, as well as Bayesian models that incorporate deep learning elements [1-11]. In fact, the use of Bayesian techniques in deep learning can be traced back to the 1990s’, in seminal works by Radford Neal [12], David MacKay [13], and Dayan et al. [14]. These gave us tools to reason about deep models’ confidence, and achieved state-of-the-art performance on many tasks. However earlier tools did not adapt when new needs arose (such as scalability to big data), and were consequently forgotten. Such ideas are now being revisited in light of new advances in the field, yielding many exciting new results.
Extending on last year’s workshop’s success, this workshop will again study the advantages and disadvantages of such ideas, and will be a platform to host the recent flourish of ideas using Bayesian approaches in deep learning and using deep learning tools in Bayesian modelling. The program includes a mix of invited talks, contributed talks, and contributed posters. It will be composed of five main themes: deep generative models, variational inference using neural network recognition models, practical approximate inference techniques in Bayesian neural networks, applications of Bayesian neural networks, and information theory in deep learning. Future directions for the field will be debated in a panel discussion.
Topics:
Probabilistic deep models for classification and regression (such as extensions and application of Bayesian neural networks),
Generative deep models (such as variational autoencoders),
Incorporating explicit prior knowledge in deep learning (such as posterior regularization with logic rules),
Approximate inference for Bayesian deep learning (such as variational Bayes / expectation propagation / etc. in Bayesian neural networks),
Scalable MCMC inference in Bayesian deep models,
Deep recognition models for variational inference (amortized inference),
Model uncertainty in deep learning,
Bayesian deep reinforcement learning,
Deep learning with small data,
Deep learning in Bayesian modelling,
Probabilistic semi-supervised learning techniques,
Active learning and Bayesian optimization for experimental design,
Applying non-parametric methods, one-shot learning, and Bayesian deep learning in general,
Implicit inference,
Kernel methods in Bayesian deep learning.
References:
[1] - Kingma, DP and Welling, M, ‘’Auto-encoding variational bayes’’, 2013.
[2] - Rezende, D, Mohamed, S, and Wierstra, D, ‘’Stochastic backpropagation and approximate inference in deep generative models’’, 2014.
[3] - Blundell, C, Cornebise, J, Kavukcuoglu, K, and Wierstra, D, ‘’Weight uncertainty in neural network’’, 2015.
[4] - Hernandez-Lobato, JM and Adams, R, ’’Probabilistic backpropagation for scalable learning of Bayesian neural networks’’, 2015.
[5] - Gal, Y and Ghahramani, Z, ‘’Dropout as a Bayesian approximation: Representing model uncertainty in deep learning’’, 2015.
[6] - Gal, Y and Ghahramani, G, ‘’Bayesian convolutional neural networks with Bernoulli approximate variational inference’’, 2015.
[7] - Kingma, D, Salimans, T, and Welling, M. ‘’Variational dropout and the local reparameterization trick’’, 2015.
[8] - Balan, AK, Rathod, V, Murphy, KP, and Welling, M, ‘’Bayesian dark knowledge’’, 2015.
[9] - Louizos, C and Welling, M, “Structured and Efficient Variational Deep Learning with Matrix Gaussian Posteriors”, 2016.
[10] - Lawrence, ND and Quinonero-Candela, J, “Local distance preservation in the GP-LVM through back constraints”, 2006.
[11] - Tran, D, Ranganath, R, and Blei, DM, “Variational Gaussian Process”, 2015.
[12] - Neal, R, ‘’Bayesian Learning for Neural Networks’’, 1996.
[13] - MacKay, D, ‘’A practical Bayesian framework for backpropagation networks‘’, 1992.
[14] - Dayan, P, Hinton, G, Neal, R, and Zemel, S, ‘’The Helmholtz machine’’, 1995.
[15] - Wilson, AG, Hu, Z, Salakhutdinov, R, and Xing, EP, “Deep Kernel Learning”, 2016.
[16] - Saatchi, Y and Wilson, AG, “Bayesian GAN”, 2017.
[17] - MacKay, D.J.C. “Bayesian Methods for Adaptive Models”, PhD thesis, 1992.
Sat 8:05 a.m. - 8:30 a.m.
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Deep Probabilistic Programming
(Invited talk)
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Dustin Tran 🔗 |
Sat 8:30 a.m. - 8:45 a.m.
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TBD 1
(Contributed talk)
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🔗 |
Sat 8:45 a.m. - 9:10 a.m.
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Automatic Model Selection in BNNs with Horseshoe Priors
(Invited talk)
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Finale Doshi-Velez 🔗 |
Sat 9:10 a.m. - 9:40 a.m.
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Deep Bayes for Distributed Learning, Uncertainty Quantification and Compression
(Special talk)
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Max Welling 🔗 |
Sat 9:40 a.m. - 9:55 a.m.
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Poster spotlights
(Spotlights)
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🔗 |
Sat 9:55 a.m. - 10:55 a.m.
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Discussion over coffee and poster session 1
(Break)
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🔗 |
Sat 10:55 a.m. - 11:20 a.m.
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Stochastic Gradient Descent as Approximate Bayesian Inference
(Invited talk)
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🔗 |
Sat 11:20 a.m. - 11:35 a.m.
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TBD 2
(Contributed talk)
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🔗 |
Sat 11:35 a.m. - 12:00 p.m.
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TBD 2.5
(Invited talk)
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Nal Kalchbrenner 🔗 |
Sat 12:00 p.m. - 1:35 p.m.
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Lunch
(Break)
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🔗 |
Sat 1:35 p.m. - 2:00 p.m.
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Deep Kernel Learning
(Invited talk)
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Ruslan Salakhutdinov 🔗 |
Sat 2:00 p.m. - 2:15 p.m.
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TBD 3
(Contributed talk)
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🔗 |
Sat 2:15 p.m. - 2:40 p.m.
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Bayes by Backprop
(Invited talk)
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Meire Fortunato 🔗 |
Sat 2:40 p.m. - 3:35 p.m.
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Discussion over coffee and poster session 2
(Break)
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🔗 |
Sat 3:35 p.m. - 4:00 p.m.
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How do the Deep Learning layers converge to the Information Bottleneck limit by Stochastic Gradient Descent?
(Invited talk)
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Naftali Tishby 🔗 |
Sat 4:00 p.m. - 5:00 p.m.
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Panel Session
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Neil Lawrence · Finale Doshi-Velez · Zoubin Ghahramani · Yann LeCun · Max Welling · Yee Whye Teh · Ole Winther 🔗 |
Sat 5:00 p.m. - 6:00 p.m.
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Poster session
(Break)
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Xun Zheng · Tim G. J. Rudner · Christopher Tegho · Patrick McClure · Yunhao Tang · ASHWIN D'CRUZ · Juan Camilo Gamboa Higuera · Chandra Sekhar Seelamantula · Jhosimar Arias Figueroa · Andrew Berlin · Maxime Voisin · Alexander Amini · Thang Long Doan · Hengyuan Hu · Aleksandar Botev · Niko Suenderhauf · CHI ZHANG · John Lambert
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Author Information
Yarin Gal (University of Oxford)
José Miguel Hernández-Lobato (University of Cambridge)
Christos Louizos (University of Amsterdam)
Andrew Wilson (Cornell University)
Andrew Wilson (Cornell University)

I am a professor of machine learning at New York University.
Diederik Kingma (Google)
Zoubin Ghahramani (Uber and University of Cambridge)
Zoubin Ghahramani is Professor of Information Engineering at the University of Cambridge, where he leads the Machine Learning Group. He studied computer science and cognitive science at the University of Pennsylvania, obtained his PhD from MIT in 1995, and was a postdoctoral fellow at the University of Toronto. His academic career includes concurrent appointments as one of the founding members of the Gatsby Computational Neuroscience Unit in London, and as a faculty member of CMU's Machine Learning Department for over 10 years. His current research interests include statistical machine learning, Bayesian nonparametrics, scalable inference, probabilistic programming, and building an automatic statistician. He has held a number of leadership roles as programme and general chair of the leading international conferences in machine learning including: AISTATS (2005), ICML (2007, 2011), and NIPS (2013, 2014). In 2015 he was elected a Fellow of the Royal Society.
Kevin Murphy (Google)
Max Welling (University of Amsterdam / Qualcomm AI Research)
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Andrew Wilson · Jason Yosinski · Patrice Simard · Rich Caruana · William Herlands -
2017 Symposium: Kinds of intelligence: types, tests and meeting the needs of society »
José Hernández-Orallo · Zoubin Ghahramani · Tomaso Poggio · Adrian Weller · Matthew Crosby -
2017 Poster: Concrete Dropout »
Yarin Gal · Jiri Hron · Alex Kendall -
2017 Poster: Bayesian GAN »
Yunus Saatci · Andrew Wilson -
2017 Poster: Causal Effect Inference with Deep Latent-Variable Models »
Christos Louizos · Uri Shalit · Joris Mooij · David Sontag · Richard Zemel · Max Welling -
2017 Spotlight: Bayesian GANs »
Yunus Saatci · Andrew Wilson -
2017 Poster: Bayesian Optimization with Gradients »
Jian Wu · Matthias Poloczek · Andrew Wilson · Peter Frazier -
2017 Poster: What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision? »
Alex Kendall · Yarin Gal -
2017 Poster: Scalable Log Determinants for Gaussian Process Kernel Learning »
Kun Dong · David Eriksson · Hannes Nickisch · David Bindel · Andrew Wilson -
2017 Spotlight: What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision? »
Alex Kendall · Yarin Gal -
2017 Oral: Bayesian Optimization with Gradients »
Jian Wu · Matthias Poloczek · Andrew Wilson · Peter Frazier -
2017 Poster: Bayesian Compression for Deep Learning »
Christos Louizos · Karen Ullrich · Max Welling -
2017 Poster: Interpolated Policy Gradient: Merging On-Policy and Off-Policy Gradient Estimation for Deep Reinforcement Learning »
Shixiang (Shane) Gu · Timothy Lillicrap · Richard Turner · Zoubin Ghahramani · Bernhard Schölkopf · Sergey Levine -
2017 Poster: Scalable Levy Process Priors for Spectral Kernel Learning »
Phillip Jang · Andrew Loeb · Matthew Davidow · Andrew Wilson -
2017 Poster: Real Time Image Saliency for Black Box Classifiers »
Piotr Dabkowski · Yarin Gal -
2016 : Panel Discussion »
Shakir Mohamed · David Blei · Ryan Adams · José Miguel Hernández-Lobato · Ian Goodfellow · Yarin Gal -
2016 : Automatic Chemical Design using Variational Autoencoders »
José Miguel Hernández-Lobato -
2016 : Alpha divergence minimization for Bayesian deep learning »
José Miguel Hernández-Lobato -
2016 : Max Welling : Making Deep Learning Efficient Through Sparsification »
Max Welling -
2016 : Automatic Discovery of the Statistical Types of Variables in a Dataset »
Isabel Valera · Zoubin Ghahramani -
2016 : History of Bayesian neural networks »
Zoubin Ghahramani -
2016 Workshop: Bayesian Deep Learning »
Yarin Gal · Christos Louizos · Zoubin Ghahramani · Kevin Murphy · Max Welling -
2016 Workshop: Towards an Artificial Intelligence for Data Science »
Charles Sutton · James Geddes · Zoubin Ghahramani · Padhraic Smyth · Chris Williams -
2016 : How Machine Learning Research Can Address Key Societal and Governance Issues »
Zoubin Ghahramani -
2016 Workshop: People and machines: Public views on machine learning, and what this means for machine learning researchers »
Susannah Odell · Peter Donnelly · Jessica Montgomery · Sabine Hauert · Zoubin Ghahramani · Katherine Gorman -
2016 Workshop: Advances in Approximate Bayesian Inference »
Tamara Broderick · Stephan Mandt · James McInerney · Dustin Tran · David Blei · Kevin Murphy · Andrew Gelman · Michael I Jordan -
2016 Workshop: Interpretable Machine Learning for Complex Systems »
Andrew Wilson · Been Kim · William Herlands -
2016 Poster: A Theoretically Grounded Application of Dropout in Recurrent Neural Networks »
Yarin Gal · Zoubin Ghahramani -
2016 Poster: Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural Networks »
Tim Salimans · Diederik Kingma -
2016 Oral: Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural Networks »
Tim Salimans · Diederik Kingma -
2016 Poster: Improving Variational Autoencoders with Inverse Autoregressive Flow »
Diederik Kingma · Tim Salimans · Rafal Jozefowicz · Peter Chen · Xi Chen · Ilya Sutskever · Max Welling -
2016 Poster: Stochastic Variational Deep Kernel Learning »
Andrew Wilson · Zhiting Hu · Russ Salakhutdinov · Eric Xing -
2016 Poster: Distributed Flexible Nonlinear Tensor Factorization »
Shandian Zhe · Kai Zhang · Pengyuan Wang · Kuang-chih Lee · Zenglin Xu · Yuan Qi · Zoubin Ghahramani -
2015 : Variational Auto-Encoders and Extensions »
Diederik Kingma -
2015 : Bayesian Optimization »
Zoubin Ghahramani · Bobak Shahriari -
2015 Workshop: Black box learning and inference »
Josh Tenenbaum · Jan-Willem van de Meent · Tejas Kulkarni · S. M. Ali Eslami · Brooks Paige · Frank Wood · Zoubin Ghahramani -
2015 Workshop: Scalable Monte Carlo Methods for Bayesian Analysis of Big Data »
Babak Shahbaba · Yee Whye Teh · Max Welling · Arnaud Doucet · Christophe Andrieu · Sebastian J. Vollmer · Pierre Jacob -
2015 : *Max Welling* Optimization Monte Carlo »
Max Welling -
2015 Workshop: Nonparametric Methods for Large Scale Representation Learning »
Andrew G Wilson · Alexander Smola · Eric Xing -
2015 Symposium: Deep Learning Symposium »
Yoshua Bengio · Marc'Aurelio Ranzato · Honglak Lee · Max Welling · Andrew Y Ng -
2015 Poster: Particle Gibbs for Infinite Hidden Markov Models »
Nilesh Tripuraneni · Shixiang (Shane) Gu · Hong Ge · Zoubin Ghahramani -
2015 Poster: Neural Adaptive Sequential Monte Carlo »
Shixiang (Shane) Gu · Zoubin Ghahramani · Richard Turner -
2015 Poster: Bayesian dark knowledge »
Anoop Korattikara Balan · Vivek Rathod · Kevin Murphy · Max Welling -
2015 Poster: MCMC for Variationally Sparse Gaussian Processes »
James Hensman · Alexander Matthews · Maurizio Filippone · Zoubin Ghahramani -
2015 Poster: Optimization Monte Carlo: Efficient and Embarrassingly Parallel Likelihood-Free Inference »
Ted Meeds · Max Welling -
2015 Poster: Parallel Predictive Entropy Search for Batch Global Optimization of Expensive Objective Functions »
Amar Shah · Zoubin Ghahramani -
2015 Poster: The Human Kernel »
Andrew Wilson · Christoph Dann · Chris Lucas · Eric Xing -
2015 Poster: Stochastic Expectation Propagation »
Yingzhen Li · José Miguel Hernández-Lobato · Richard Turner -
2015 Spotlight: The Human Kernel »
Andrew Wilson · Christoph Dann · Chris Lucas · Eric Xing -
2015 Spotlight: Stochastic Expectation Propagation »
Yingzhen Li · José Miguel Hernández-Lobato · Richard Turner -
2015 Invited Talk: Probabilistic Machine Learning: Foundations and Frontiers »
Zoubin Ghahramani -
2015 Poster: Statistical Model Criticism using Kernel Two Sample Tests »
James R Lloyd · Zoubin Ghahramani -
2015 Poster: Variational Dropout and the Local Reparameterization Trick »
Diederik Kingma · Tim Salimans · Max Welling -
2014 Workshop: Modern Nonparametrics 3: Automating the Learning Pipeline »
Eric Xing · Mladen Kolar · Arthur Gretton · Samory Kpotufe · Han Liu · Zoltán Szabó · Alan Yuille · Andrew G Wilson · Ryan Tibshirani · Sasha Rakhlin · Damian Kozbur · Bharath Sriperumbudur · David Lopez-Paz · Kirthevasan Kandasamy · Francesco Orabona · Andreas Damianou · Wacha Bounliphone · Yanshuai Cao · Arijit Das · Yingzhen Yang · Giulia DeSalvo · Dmitry Storcheus · Roberto Valerio -
2014 Workshop: Bayesian Optimization in Academia and Industry »
Zoubin Ghahramani · Ryan Adams · Matthew Hoffman · Kevin Swersky · Jasper Snoek -
2014 Workshop: ABC in Montreal »
Max Welling · Neil D Lawrence · Richard D Wilkinson · Ted Meeds · Christian X Robert -
2014 Poster: Distributed Variational Inference in Sparse Gaussian Process Regression and Latent Variable Models »
Yarin Gal · Mark van der Wilk · Carl Edward Rasmussen -
2014 Poster: Predictive Entropy Search for Efficient Global Optimization of Black-box Functions »
José Miguel Hernández-Lobato · Matthew Hoffman · Zoubin Ghahramani -
2014 Poster: Semi-supervised Learning with Deep Generative Models »
Diederik Kingma · Shakir Mohamed · Danilo Jimenez Rezende · Max Welling -
2014 Poster: Fast Kernel Learning for Multidimensional Pattern Extrapolation »
Andrew Wilson · Elad Gilboa · John P Cunningham · Arye Nehorai -
2014 Poster: Gaussian Process Volatility Model »
Yue Wu · José Miguel Hernández-Lobato · Zoubin Ghahramani -
2014 Demonstration: Machine Learning in the Browser »
Ted Meeds · Remco Hendriks · Said Al Faraby · Magiel Bruntink · Max Welling -
2014 Spotlight: Semi-supervised Learning with Deep Generative Models »
Diederik Kingma · Shakir Mohamed · Danilo Jimenez Rezende · Max Welling -
2014 Spotlight: Predictive Entropy Search for Efficient Global Optimization of Black-box Functions »
José Miguel Hernández-Lobato · Matthew Hoffman · Zoubin Ghahramani -
2014 Poster: General Table Completion using a Bayesian Nonparametric Model »
Isabel Valera · Zoubin Ghahramani -
2013 Workshop: Probabilistic Models for Big Data »
Neil D Lawrence · Joaquin Quiñonero-Candela · Tianshi Gao · James Hensman · Zoubin Ghahramani · Max Welling · David Blei · Ralf Herbrich -
2013 Poster: Learning Feature Selection Dependencies in Multi-task Learning »
Daniel Hernández-lobato · José Miguel Hernández-Lobato -
2013 Poster: Gaussian Process Conditional Copulas with Applications to Financial Time Series »
José Miguel Hernández-Lobato · James R Lloyd · Daniel Hernández-lobato -
2013 Session: Oral Session 5 »
Zoubin Ghahramani -
2012 Poster: Collaborative Gaussian Processes for Preference Learning »
Neil Houlsby · José Miguel Hernández-Lobato · Ferenc Huszar · Zoubin Ghahramani -
2012 Poster: A nonparametric variable clustering model »
David A Knowles · Konstantina Palla · Zoubin Ghahramani -
2012 Poster: Semi-Supervised Domain Adaptation with Non-Parametric Copulas »
David Lopez-Paz · José Miguel Hernández-Lobato · Bernhard Schölkopf -
2012 Poster: Random function priors for exchangeable graphs and arrays »
James R Lloyd · Daniel Roy · Peter Orbanz · Zoubin Ghahramani -
2012 Poster: Active Learning of Model Evidence Using Bayesian Quadrature »
Michael A Osborne · David Duvenaud · Roman Garnett · Carl Edward Rasmussen · Stephen J Roberts · Zoubin Ghahramani -
2012 Poster: Continuous Relaxations for Discrete Hamiltonian Monte Carlo »
Zoubin Ghahramani · Yichuan Zhang · Charles Sutton · Amos Storkey -
2012 Spotlight: Semi-Supervised Domain Adaptation with Non-Parametric Copulas »
David Lopez-Paz · José Miguel Hernández-Lobato · Bernhard Schölkopf -
2012 Spotlight: Continuous Relaxations for Discrete Hamiltonian Monte Carlo »
Zoubin Ghahramani · Yichuan Zhang · Charles Sutton · Amos Storkey -
2012 Poster: The Time-Marginalized Coalescent Prior for Hierarchical Clustering »
Levi Boyles · Max Welling -
2011 Workshop: Copulas in Machine Learning »
Gal Elidan · Zoubin Ghahramani · John Lafferty -
2011 Poster: Testing a Bayesian Measure of Representativeness Using a Large Image Database »
Joshua T Abbott · Katherine Heller · Zoubin Ghahramani · Tom Griffiths -
2011 Poster: Statistical Tests for Optimization Efficiency »
Levi Boyles · Anoop Korattikara · Deva Ramanan · Max Welling -
2011 Poster: Robust Multi-Class Gaussian Process Classification »
Daniel Hernández-lobato · José Miguel Hernández-Lobato · Pierre Dupont -
2010 Workshop: Transfer Learning Via Rich Generative Models. »
Russ Salakhutdinov · Ryan Adams · Josh Tenenbaum · Zoubin Ghahramani · Tom Griffiths -
2010 Talk: Unifying Views in Unsupervised Learning »
Zoubin Ghahramani -
2010 Oral: Tree-Structured Stick Breaking for Hierarchical Data »
Ryan Adams · Zoubin Ghahramani · Michael Jordan -
2010 Poster: Tree-Structured Stick Breaking for Hierarchical Data »
Ryan Adams · Zoubin Ghahramani · Michael Jordan -
2010 Poster: On Herding and the Perceptron Cycling Theorem »
Andrew E Gelfand · Yutian Chen · Laurens van der Maaten · Max Welling -
2010 Spotlight: Copula Processes »
Andrew Wilson · Zoubin Ghahramani -
2010 Poster: Copula Processes »
Andrew Wilson · Zoubin Ghahramani -
2010 Poster: Regularized estimation of image statistics by Score Matching »
Diederik Kingma · Yann LeCun -
2009 Workshop: Nonparametric Bayes »
Dilan Gorur · Francois Caron · Yee Whye Teh · David B Dunson · Zoubin Ghahramani · Michael Jordan -
2009 Poster: Large Scale Nonparametric Bayesian Inference: Data Parallelisation in the Indian Buffet Process »
Shakir Mohamed · David A Knowles · Zoubin Ghahramani · Finale P Doshi-Velez -
2008 Session: Oral session 10: Nonparametric Processes, Scene Processing and Image Statistics »
Max Welling -
2008 Poster: The Infinite Factorial Hidden Markov Model »
Jurgen Van Gael · Yee Whye Teh · Zoubin Ghahramani -
2008 Poster: Bayesian Exponential Family PCA »
Shakir Mohamed · Katherine Heller · Zoubin Ghahramani -
2008 Poster: Asynchronous Distributed Learning of Topic Models »
Arthur Asuncion · Padhraic Smyth · Max Welling -
2008 Spotlight: Bayesian Exponential Family PCA »
Shakir Mohamed · Katherine Heller · Zoubin Ghahramani -
2008 Spotlight: The Infinite Factorial Hidden Markov Model »
Jurgen Van Gael · Yee Whye Teh · Zoubin Ghahramani -
2007 Spotlight: Collapsed Variational Inference for HDP »
Yee Whye Teh · Kenichi Kurihara · Max Welling -
2007 Spotlight: Distributed Inference for Latent Dirichlet Allocation »
David Newman · Arthur Asuncion · Padhraic Smyth · Max Welling -
2007 Poster: Infinite State Bayes-Nets for Structured Domains »
Max Welling · Ian Porteous · Evgeniy Bart -
2007 Poster: Hidden Common Cause Relations in Relational Learning »
Ricardo Silva · Wei Chu · Zoubin Ghahramani -
2007 Poster: Collapsed Variational Inference for HDP »
Yee Whye Teh · Kenichi Kurihara · Max Welling -
2007 Poster: Distributed Inference for Latent Dirichlet Allocation »
David Newman · Arthur Asuncion · Padhraic Smyth · Max Welling -
2007 Poster: Regulator Discovery from Gene Expression Time Series of Malaria Parasites: a Hierachical Approach »
José Miguel Hernández-Lobato · Tjeerd M Dijkstra · Tom Heskes -
2007 Spotlight: Infinite State Bayes-Nets for Structured Domains »
Max Welling · Ian Porteous · Evgeniy Bart -
2007 Spotlight: Hidden Common Cause Relations in Relational Learning »
Ricardo Silva · Wei Chu · Zoubin Ghahramani -
2006 Poster: Relational Learning with Gaussian Processes »
Wei Chu · Vikas Sindhwani · Zoubin Ghahramani · Sathiya Selvaraj Keerthi -
2006 Poster: Structure Learning in Markov Random Fields »
Sridevi Parise · Max Welling -
2006 Poster: Accelerated Variational Dirichlet Process Mixtures »
Kenichi Kurihara · Max Welling · Nikos Vlassis -
2006 Poster: Modeling Dyadic Data with Binary Latent Features »
Ted Meeds · Zoubin Ghahramani · Radford M Neal · Sam T Roweis -
2006 Spotlight: Accelerated Variational Dirichlet Process Mixtures »
Kenichi Kurihara · Max Welling · Nikos Vlassis -
2006 Spotlight: Modeling Dyadic Data with Binary Latent Features »
Ted Meeds · Zoubin Ghahramani · Radford M Neal · Sam T Roweis -
2006 Poster: A Collapsed Variational Bayesian Inference Algorithm for Latent Dirichlet Allocation »
Yee Whye Teh · David Newman · Max Welling