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One of the major problems driving current research in statistical machine learning is the search for ways to exploit highly-structured models that are both expressive and tractable. Bayesian nonparametrics (BNP) provides a framework for developing robust and flexible models that can accurately represent the complex structure in the data. Model flexibility is achieved by assigning priors with unbounded capacity and overfitting is prevented by the Bayesian approach of integrating out all parameters and latent variables. Inference is typically achieves with approximation techniques like Markov chain Monte Carlo and variational Bayes. As a result, the model can automatically infer the necessary amount of complexity required for modeling the given data.
Author Information
Dilan Gorur (DeepMind)
Francois Caron (University of Oxford)
Yee Whye Teh (University of Oxford, DeepMind)
I am a Professor of Statistical Machine Learning at the Department of Statistics, University of Oxford and a Research Scientist at DeepMind. I am also an Alan Turing Institute Fellow and a European Research Council Consolidator Fellow. I obtained my Ph.D. at the University of Toronto (working with Geoffrey Hinton), and did postdoctoral work at the University of California at Berkeley (with Michael Jordan) and National University of Singapore (as Lee Kuan Yew Postdoctoral Fellow). I was a Lecturer then a Reader at the Gatsby Computational Neuroscience Unit, UCL, and a tutorial fellow at University College Oxford, prior to my current appointment. I am interested in the statistical and computational foundations of intelligence, and works on scalable machine learning, probabilistic models, Bayesian nonparametrics and deep learning. I was programme co-chair of ICML 2017 and AISTATS 2010.
David B Dunson (Duke University)
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.
Michael Jordan (UC Berkeley)
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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 : Random Tensor Decompositions for Regression and Collaborative Filtering »
Yee Whye Teh -
2015 Poster: Parallelizing MCMC with Random Partition Trees »
Xiangyu Wang · Fangjian Guo · Katherine Heller · David B Dunson -
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: Variational Consensus Monte Carlo »
Maxim Rabinovich · Elaine Angelino · Michael Jordan -
2015 Poster: MCMC for Variationally Sparse Gaussian Processes »
James Hensman · Alexander Matthews · Maurizio Filippone · Zoubin Ghahramani -
2015 Poster: On the consistency theory of high dimensional variable screening »
Xiangyu Wang · Chenlei Leng · David B Dunson -
2015 Poster: Probabilistic Curve Learning: Coulomb Repulsion and the Electrostatic Gaussian Process »
Ye Wang · David B Dunson -
2015 Poster: On the Accuracy of Self-Normalized Log-Linear Models »
Jacob Andreas · Maxim Rabinovich · Michael Jordan · Dan Klein -
2015 Poster: A hybrid sampler for Poisson-Kingman mixture models »
Maria Lomeli · Stefano Favaro · Yee Whye Teh -
2015 Poster: Parallel Predictive Entropy Search for Batch Global Optimization of Expensive Objective Functions »
Amar Shah · Zoubin Ghahramani -
2015 Poster: Linear Response Methods for Accurate Covariance Estimates from Mean Field Variational Bayes »
Ryan Giordano · Tamara Broderick · Michael Jordan -
2015 Spotlight: Linear Response Methods for Accurate Covariance Estimates from Mean Field Variational Bayes »
Ryan Giordano · Tamara Broderick · Michael Jordan -
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: Expectation Particle Belief Propagation »
Thibaut Lienart · Yee Whye Teh · Arnaud Doucet -
2014 Workshop: Advances in Variational Inference »
David Blei · Shakir Mohamed · Michael Jordan · Charles Blundell · Tamara Broderick · Matthew D. Hoffman -
2014 Workshop: Personalization: Methods and Applications »
Yisong Yue · Khalid El-Arini · Dilan Gorur -
2014 Workshop: Bayesian Optimization in Academia and Industry »
Zoubin Ghahramani · Ryan Adams · Matthew Hoffman · Kevin Swersky · Jasper Snoek -
2014 Poster: Communication-Efficient Distributed Dual Coordinate Ascent »
Martin Jaggi · Virginia Smith · Martin Takac · Jonathan Terhorst · Sanjay Krishnan · Thomas Hofmann · Michael Jordan -
2014 Poster: Distributed Bayesian Posterior Sampling via Moment Sharing »
Minjie Xu · Balaji Lakshminarayanan · Yee Whye Teh · Jun Zhu · Bo Zhang -
2014 Poster: Asynchronous Anytime Sequential Monte Carlo »
Brooks Paige · Frank Wood · Arnaud Doucet · Yee Whye Teh -
2014 Poster: Spectral Methods meet EM: A Provably Optimal Algorithm for Crowdsourcing »
Yuchen Zhang · Xi Chen · Denny Zhou · Michael Jordan -
2014 Poster: Parallel Double Greedy Submodular Maximization »
Xinghao Pan · Stefanie Jegelka · Joseph Gonzalez · Joseph K Bradley · Michael Jordan -
2014 Oral: Asynchronous Anytime Sequential Monte Carlo »
Brooks Paige · Frank Wood · Arnaud Doucet · Yee Whye Teh -
2014 Spotlight: Spectral Methods meet EM: A Provably Optimal Algorithm for Crowdsourcing »
Yuchen Zhang · Xi Chen · Denny Zhou · Michael Jordan -
2014 Poster: Median Selection Subset Aggregation for Parallel Inference »
Xiangyu Wang · Peichao Peng · David B Dunson -
2014 Poster: Predictive Entropy Search for Efficient Global Optimization of Black-box Functions »
José Miguel Hernández-Lobato · Matthew Hoffman · Zoubin Ghahramani -
2014 Poster: Gaussian Process Volatility Model »
Yue Wu · José Miguel Hernández-Lobato · Zoubin Ghahramani -
2014 Oral: Median Selection Subset Aggregation for Parallel Inference »
Xiangyu Wang · Peichao Peng · David B Dunson -
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 -
2014 Poster: Mondrian Forests: Efficient Online Random Forests »
Balaji Lakshminarayanan · Daniel Roy · Yee Whye Teh -
2014 Poster: On the Convergence Rate of Decomposable Submodular Function Minimization »
Robert Nishihara · Stefanie Jegelka · Michael Jordan -
2013 Workshop: Big Learning : Advances in Algorithms and Data Management »
Xinghao Pan · Haijie Gu · Joseph Gonzalez · Sameer Singh · Yucheng Low · Joseph Hellerstein · Derek G Murray · Raghu Ramakrishnan · Michael Jordan · Christopher Ré -
2013 Workshop: Discrete Optimization in Machine Learning: Connecting Theory and Practice »
Stefanie Jegelka · Andreas Krause · Pradeep Ravikumar · Kazuo Murota · Jeffrey A Bilmes · Yisong Yue · Michael Jordan -
2013 Workshop: What Difference Does Personalization Make? »
Dilan Gorur · Romer Rosales · Olivier Chapelle · Dorota Glowacka -
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 Session: Oral Session 10 »
Michael Jordan -
2013 Poster: A Comparative Framework for Preconditioned Lasso Algorithms »
Fabian L Wauthier · Nebojsa Jojic · Michael Jordan -
2013 Poster: Locally Adaptive Bayesian Multivariate Time Series »
Daniele Durante · Bruno Scarpa · David B Dunson -
2013 Poster: Learning with Invariance via Linear Functionals on Reproducing Kernel Hilbert Space »
Xinhua Zhang · Wee Sun Lee · Yee Whye Teh -
2013 Poster: Information-theoretic lower bounds for distributed statistical estimation with communication constraints »
Yuchen Zhang · John Duchi · Michael Jordan · Martin J Wainwright -
2013 Oral: Information-theoretic lower bounds for distributed statistical estimation with communication constraints »
Yuchen Zhang · John Duchi · Michael Jordan · Martin J Wainwright -
2013 Spotlight: Learning with Invariance via Linear Functionals on Reproducing Kernel Hilbert Space »
Xinhua Zhang · Wee Sun Lee · Yee Whye Teh -
2013 Session: Oral Session 5 »
Zoubin Ghahramani -
2013 Poster: Optimistic Concurrency Control for Distributed Unsupervised Learning »
Xinghao Pan · Joseph Gonzalez · Stefanie Jegelka · Tamara Broderick · Michael Jordan -
2013 Poster: Bayesian Hierarchical Community Discovery »
Charles Blundell · Yee Whye Teh -
2013 Poster: Stochastic Gradient Riemannian Langevin Dynamics on the Probability Simplex »
Sam Patterson · Yee Whye Teh -
2013 Spotlight: Stochastic Gradient Riemannian Langevin Dynamics on the Probability Simplex »
Sam Patterson · Yee Whye Teh -
2013 Poster: Probabilistic Low-Rank Matrix Completion with Adaptive Spectral Regularization Algorithms »
Adrien Todeschini · Francois Caron · Marie Chavent -
2013 Poster: Local Privacy and Minimax Bounds: Sharp Rates for Probability Estimation »
John Duchi · Martin J Wainwright · Michael Jordan -
2013 Poster: Streaming Variational Bayes »
Tamara Broderick · Nicholas Boyd · Andre Wibisono · Ashia C Wilson · Michael Jordan -
2013 Poster: Multiscale Dictionary Learning for Estimating Conditional Distributions »
Francesca Petralia · Joshua T Vogelstein · David B Dunson -
2013 Poster: Estimation, Optimization, and Parallelism when Data is Sparse »
John Duchi · Michael Jordan · Brendan McMahan -
2012 Workshop: Bayesian Nonparametric Models For Reliable Planning And Decision-Making Under Uncertainty »
Jonathan How · Lawrence Carin · John Fisher III · Michael Jordan · Alborz Geramifard -
2012 Poster: Multiresolution Gaussian Processes »
Emily Fox · David B Dunson -
2012 Poster: Searching for objects driven by context »
Bogdan Alexe · Nicolas Heess · Yee Whye Teh · Vittorio Ferrari -
2012 Poster: Collaborative Gaussian Processes for Preference Learning »
Neil Houlsby · José Miguel Hernández-Lobato · Ferenc Huszar · Zoubin Ghahramani -
2012 Poster: Bayesian nonparametric models for bipartite graphs »
Francois Caron -
2012 Poster: Learning Label Trees for Probabilistic Modelling of Implicit Feedback »
Andriy Mnih · Yee Whye Teh -
2012 Poster: Privacy Aware Learning »
John Duchi · Michael Jordan · Martin J Wainwright -
2012 Poster: MCMC for continuous-time discrete-state systems »
Vinayak Rao · Yee Whye Teh -
2012 Poster: Bayesian nonparametric models for ranked data »
Francois Caron · Yee Whye Teh -
2012 Poster: Ancestor Sampling for Particle Gibbs »
Fredrik Lindsten · Michael Jordan · Thomas Schön -
2012 Poster: A nonparametric variable clustering model »
David A Knowles · Konstantina Palla · Zoubin Ghahramani -
2012 Oral: Bayesian nonparametric models for bipartite graphs »
Francois Caron -
2012 Oral: Privacy Aware Learning »
John Duchi · Michael Jordan · Martin J Wainwright -
2012 Poster: Finite Sample Convergence Rates of Zero-Order Stochastic Optimization Methods »
John Duchi · Michael Jordan · Martin J Wainwright · Andre Wibisono -
2012 Poster: Random function priors for exchangeable graphs and arrays »
James R Lloyd · Daniel Roy · Peter Orbanz · Zoubin Ghahramani -
2012 Poster: Repulsive Mixtures »
FRANCESCA PETRALIA · Vinayak Rao · David B Dunson -
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: Small-Variance Asymptotics for Exponential Family Dirichlet Process Mixture Models »
Ke Jiang · Brian Kulis · Michael Jordan -
2012 Poster: Continuous Relaxations for Discrete Hamiltonian Monte Carlo »
Zoubin Ghahramani · Yichuan Zhang · Charles Sutton · Amos Storkey -
2012 Spotlight: Searching for objects driven by context »
Bogdan Alexe · Nicolas Heess · Yee Whye Teh · Vittorio Ferrari -
2012 Spotlight: Continuous Relaxations for Discrete Hamiltonian Monte Carlo »
Zoubin Ghahramani · Yichuan Zhang · Charles Sutton · Amos Storkey -
2012 Poster: Scalable imputation of genetic data with a discrete fragmentation-coagulation process »
Lloyd T Elliott · Yee Whye Teh -
2011 Workshop: Copulas in Machine Learning »
Gal Elidan · Zoubin Ghahramani · John Lafferty -
2011 Workshop: Big Learning: Algorithms, Systems, and Tools for Learning at Scale »
Joseph E Gonzalez · Sameer Singh · Graham Taylor · James Bergstra · Alice Zheng · Misha Bilenko · Yucheng Low · Yoshua Bengio · Michael Franklin · Carlos Guestrin · Andrew McCallum · Alexander Smola · Michael Jordan · Sugato Basu -
2011 Poster: Bayesian Bias Mitigation for Crowdsourcing »
Fabian L Wauthier · Michael Jordan -
2011 Poster: Divide-and-Conquer Matrix Factorization »
Lester W Mackey · Ameet S Talwalkar · Michael Jordan -
2011 Poster: Generalized Beta Mixtures of Gaussians »
Artin Armagan · David B Dunson · Merlise Clyde -
2011 Poster: Testing a Bayesian Measure of Representativeness Using a Large Image Database »
Joshua T Abbott · Katherine Heller · Zoubin Ghahramani · Tom Griffiths -
2011 Poster: Modelling Genetic Variations using Fragmentation-Coagulation Processes »
Yee Whye Teh · Charles Blundell · Lloyd T Elliott -
2011 Poster: The Kernel Beta Process »
Lu Ren · Yingjian Wang · David B Dunson · Lawrence Carin -
2011 Spotlight: The Kernel Beta Process »
Lu Ren · Yingjian Wang · David B Dunson · Lawrence Carin -
2011 Oral: Modelling Genetic Variations using Fragmentation-Coagulation Processes »
Yee Whye Teh · Charles Blundell · Lloyd T Elliott -
2011 Poster: Gaussian process modulated renewal processes »
Vinayak Rao · Yee Whye Teh -
2011 Poster: Hierarchical Topic Modeling for Analysis of Time-Evolving Personal Choices »
XianXing Zhang · David B Dunson · Lawrence Carin -
2011 Tutorial: Modern Bayesian Nonparametrics »
Peter Orbanz · Yee Whye Teh -
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 Invited Talk: Statistical Inference of Protein Structure and Function »
Michael Jordan -
2010 Poster: Tree-Structured Stick Breaking for Hierarchical Data »
Ryan Adams · Zoubin Ghahramani · Michael Jordan -
2010 Spotlight: Variational Inference over Combinatorial Spaces »
Alexandre Bouchard-Côté · Michael Jordan -
2010 Spotlight: Copula Processes »
Andrew Wilson · Zoubin Ghahramani -
2010 Poster: Variational Inference over Combinatorial Spaces »
Alexandre Bouchard-Côté · Michael Jordan -
2010 Poster: Joint Analysis of Time-Evolving Binary Matrices and Associated Documents »
Eric X Wang · Dehong Liu · Jorge G Silva · David B Dunson · Lawrence Carin -
2010 Poster: Copula Processes »
Andrew Wilson · Zoubin Ghahramani -
2010 Poster: Unsupervised Kernel Dimension Reduction »
Meihong Wang · Fei Sha · Michael Jordan -
2010 Poster: Improvements to the Sequence Memoizer »
Jan Gasthaus · Yee Whye Teh -
2010 Poster: Heavy-Tailed Process Priors for Selective Shrinkage »
Fabian L Wauthier · Michael Jordan -
2010 Poster: Random Conic Pursuit for Semidefinite Programming »
Ariel Kleiner · ali rahimi · Michael Jordan -
2009 Workshop: Grammar Induction, Representation of Language and Language Learning »
Alex Clark · Dorota Glowacka · John Shawe-Taylor · Yee Whye Teh · Chris J Watkins -
2009 Poster: A Bayesian Model for Simultaneous Image Clustering, Annotation and Object Segmentation »
Lan Du · Lu Ren · David B Dunson · Lawrence Carin -
2009 Poster: Bayesian Nonparametric Models on Decomposable Graphs »
Francois Caron · Arnaud Doucet -
2009 Poster: Sharing Features among Dynamical Systems with Beta Processes »
Emily Fox · Erik Sudderth · Michael Jordan · Alan S Willsky -
2009 Oral: Sharing Features among Dynamical Systems with Beta Processes »
Emily Fox · Erik Sudderth · Michael Jordan · Alan S Willsky -
2009 Poster: Indian Buffet Processes with Power-law Behavior »
Yee Whye Teh · Dilan Gorur -
2009 Spotlight: Indian Buffet Processes with Power-law Behavior »
Yee Whye Teh · Dilan Gorur -
2009 Poster: Spatial Normalized Gamma Processes »
Vinayak Rao · Yee Whye Teh -
2009 Poster: Asymptotically Optimal Regularization in Smooth Parametric Models »
Percy Liang · Francis Bach · Guillaume Bouchard · 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 -
2009 Poster: Nonparametric Latent Feature Models for Link Prediction »
Kurt T Miller · Tom Griffiths · Michael Jordan -
2009 Spotlight: Nonparametric Latent Feature Models for Link Prediction »
Kurt T Miller · Tom Griffiths · Michael Jordan -
2009 Spotlight: Spatial Normalized Gamma Processes »
Vinayak Rao · Yee Whye Teh -
2008 Oral: Shared Segmentation of Natural Scenes Using Dependent Pitman-Yor Processes »
Erik Sudderth · Michael Jordan -
2008 Oral: The Mondrian Process »
Daniel Roy · Yee Whye Teh -
2008 Poster: The Infinite Factorial Hidden Markov Model »
Jurgen Van Gael · Yee Whye Teh · Zoubin Ghahramani -
2008 Poster: Nonparametric Bayesian Learning of Switching Linear Dynamical Systems »
Emily Fox · Erik Sudderth · Michael Jordan · Alan S Willsky -
2008 Poster: Bayesian Exponential Family PCA »
Shakir Mohamed · Katherine Heller · Zoubin Ghahramani -
2008 Poster: High-dimensional union support recovery in multivariate regression »
Guillaume R Obozinski · Martin J Wainwright · Michael Jordan -
2008 Poster: The Mondrian Process »
Daniel Roy · Yee Whye Teh -
2008 Poster: Shared Segmentation of Natural Scenes Using Dependent Pitman-Yor Processes »
Erik Sudderth · Michael Jordan -
2008 Spotlight: Bayesian Exponential Family PCA »
Shakir Mohamed · Katherine Heller · Zoubin Ghahramani -
2008 Spotlight: High-dimensional union support recovery in multivariate regression »
Guillaume R Obozinski · Martin J Wainwright · Michael Jordan -
2008 Spotlight: The Infinite Factorial Hidden Markov Model »
Jurgen Van Gael · Yee Whye Teh · Zoubin Ghahramani -
2008 Spotlight: Nonparametric Bayesian Learning of Switching Linear Dynamical Systems »
Emily Fox · Erik Sudderth · Michael Jordan · Alan S Willsky -
2008 Poster: Posterior Consistency of the Silverman g-prior in Bayesian Model Choice »
Zhihua Zhang · Michael Jordan · Dit-Yan Yeung -
2008 Poster: A mixture model for the evolution of gene expression in non-homogeneous datasets »
Gerald Quon · Yee Whye Teh · Esther Chan · Michael Brudno · Tim Hughes · Quaid Morris -
2008 Poster: DiscLDA: Discriminative Learning for Dimensionality Reduction and Classification »
Simon Lacoste-Julien · Fei Sha · Michael Jordan -
2008 Poster: Dependent Dirichlet Process Spike Sorting »
Jan Gasthaus · Frank Wood · Dilan Gorur · Yee Whye Teh -
2008 Spotlight: Posterior Consistency of the Silverman g-prior in Bayesian Model Choice »
Zhihua Zhang · Michael Jordan · Dit-Yan Yeung -
2008 Poster: Efficient Inference in Phylogenetic InDel Trees »
Alexandre Bouchard-Côté · Michael Jordan · Dan Klein -
2008 Poster: Spectral Clustering with Perturbed Data »
Ling Huang · Donghui Yan · Michael Jordan · Nina Taft -
2008 Poster: An Efficient Sequential Monte Carlo Algorithm for Coalescent Clustering »
Dilan Gorur · Yee Whye Teh -
2008 Spotlight: Efficient Inference in Phylogenetic InDel Trees »
Alexandre Bouchard-Côté · Michael Jordan · Dan Klein -
2008 Spotlight: Spectral Clustering with Perturbed Data »
Ling Huang · Donghui Yan · Michael Jordan · Nina Taft -
2007 Poster: Bayesian Agglomerative Clustering with Coalescents »
Yee Whye Teh · Hal Daumé III · Daniel Roy -
2007 Poster: Cooled and Relaxed Survey Propagation for MRFs »
Hai Leong Chieu · Wee Sun Lee · Yee Whye Teh -
2007 Poster: Agreement-Based Learning »
Percy Liang · Dan Klein · Michael Jordan -
2007 Session: Session 5: Probabilistic Representations and Learning »
Yee Whye Teh -
2007 Spotlight: Cooled and Relaxed Survey Propagation for MRFs »
Hai Leong Chieu · Wee Sun Lee · Yee Whye Teh -
2007 Spotlight: Agreement-Based Learning »
Percy Liang · Dan Klein · Michael Jordan -
2007 Oral: Bayesian Agglomerative Clustering with Coalescents »
Yee Whye Teh · Hal Daumé III · Daniel Roy -
2007 Spotlight: Collapsed Variational Inference for HDP »
Yee Whye Teh · Kenichi Kurihara · Max Welling -
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 Spotlight: Hidden Common Cause Relations in Relational Learning »
Ricardo Silva · Wei Chu · Zoubin Ghahramani -
2007 Spotlight: Resampling Methods for Protein Structure Prediction with Rosetta »
Ben Blum · David Baker · Michael Jordan · Philip Bradley · Rhiju Das · David Kim -
2007 Spotlight: Estimating divergence functionals and the likelihood ratio by penalized convex risk minimization »
XuanLong Nguyen · Martin J Wainwright · Michael Jordan -
2007 Poster: Resampling Methods for Protein Structure Prediction with Rosetta »
Ben Blum · David Baker · Michael Jordan · Philip Bradley · Rhiju Das · David Kim -
2007 Poster: Estimating divergence functionals and the likelihood ratio by penalized convex risk minimization »
XuanLong Nguyen · Martin J Wainwright · Michael Jordan -
2006 Poster: Relational Learning with Gaussian Processes »
Wei Chu · Vikas Sindhwani · Zoubin Ghahramani · Sathiya Selvaraj Keerthi -
2006 Poster: Distributed PCA and Network Anomaly Detection »
Ling Huang · XuanLong Nguyen · Minos Garofalakis · Michael Jordan · Anthony D Joseph · Nina Taft -
2006 Poster: Modeling Dyadic Data with Binary Latent Features »
Ted Meeds · Zoubin Ghahramani · Radford M Neal · Sam T Roweis -
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