Timezone: »
- [ 64820 ] CoPur: Certifiably Robust Collaborative Inference via Feature Purification
- [ 64821 ] Data Augmentation MCMC for Bayesian Inference from Privatized Data
- [ 64822 ] Towards Trustworthy Automatic Diagnosis Systems by Emulating Doctors' Reasoning with Deep Reinforcement Learning
- [ 64824 ] On the Learning Mechanisms in Physical Reasoning
- [ 64825 ] Align then Fusion: Generalized Large-scale Multi-view Clustering with Anchor Matching Correspondences
- [ 64827 ] GMMSeg: Gaussian Mixture based Generative Semantic Segmentation Models
- [ 64828 ] Conformal Off-Policy Prediction in Contextual Bandits
- [ 64830 ] A Non-asymptotic Analysis of Non-parametric Temporal-Difference Learning
Q&A on RocketChat immediately following Lightning Talks
Author Information
Siwei Wang (NUDT)
Jing Liu (Mitsubishi Electric Research Laboratories (MERL))
Nianqiao Ju (Purdue University)
Shiqian Li (Peking University)
Eloïse Berthier (Inria / ENS Paris)
Muhammad Faaiz Taufiq (University of Oxford)
Arsene Fansi Tchango (Mila - Institut Québécois en Intelligence Artificielle)
Chen Liang
Chulin Xie (University of Illinois, Urbana Champaign)
Jordan Awan (Purdue University)
Jean-Francois Ton (Bytedance)
Ziad Kobeissi (INRIA)
Wenguan Wang (University of Technology Sydney)
Currently I am a Lecturer and ARC DECRA Fellow at the ReLER lab@University of Technology Sydney. My research interests lie in the intersection of computer vision, artificial intelligence, and cognition. The ultimate goal of my research is to develop a machine that can perceive, reason, and plan in real-world scenes like humans.
Xinwang Liu (National University of Defense Technology)
Kewen Wu (Tsinghua University)
Rishab Goel (Borealis AI)
Jiaxu Miao (Zhejiang University)
Suyuan Liu (National University of Defense Technology)
Julien Martel (Université de Montréal)
Ruobin Gong (Rutgers University)
Francis Bach (INRIA - Ecole Normale Superieure)
Francis Bach is a researcher at INRIA, leading since 2011 the SIERRA project-team, which is part of the Computer Science Department at Ecole Normale Supérieure in Paris, France. After completing his Ph.D. in Computer Science at U.C. Berkeley, he spent two years at Ecole des Mines, and joined INRIA and Ecole Normale Supérieure in 2007. He is interested in statistical machine learning, and especially in convex optimization, combinatorial optimization, sparse methods, kernel-based learning, vision and signal processing. He gave numerous courses on optimization in the last few years in summer schools. He has been program co-chair for the International Conference on Machine Learning in 2015.
Chi Zhang (University of California, Los Angeles)
Rob Cornish (University of Oxford)
Sanmi Koyejo (Stanford, Google Research)

Sanmi Koyejo is an Assistant Professor in the Department of Computer Science at the University of Illinois at Urbana-Champaign and a research scientist at Google AI in Accra. Koyejo's research interests are in developing the principles and practice of adaptive and robust machine learning. Additionally, Koyejo focuses on applications to biomedical imaging and neuroscience. Koyejo co-founded the Black in AI organization and currently serves on its board.
Zhi Wen (Mila)
Applied research scientist at Mila
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.
Yi Yang (Zhejiang University)
Jiaqi Jin (National University of Defense Technology)
Bo Li (UIUC)
Yixin Zhu (Peking University)
Vinayak Rao (Purdue University)
Wenxuan Tu (National University of Defense Technology)
Gaetan Marceau Caron (Mila)
Arnaud Doucet (Oxford)
Xinzhong Zhu (Zhejiang Normal University)
Joumana Ghosn (Mila)
En Zhu (National University of Defense Technology)
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Boxin Wang · Chejian Xu · Shuohang Wang · Zhe Gan · Yu Cheng · Jianfeng Gao · Ahmed Awadallah · Bo Li -
2021 Poster: Group Equivariant Subsampling »
Jin Xu · Hyunjik Kim · Thomas Rainforth · Yee Teh -
2021 Poster: G-PATE: Scalable Differentially Private Data Generator via Private Aggregation of Teacher Discriminators »
Yunhui Long · Boxin Wang · Zhuolin Yang · Bhavya Kailkhura · Aston Zhang · Carl Gunter · Bo Li -
2021 Poster: Powerpropagation: A sparsity inducing weight reparameterisation »
Jonathan Richard Schwarz · Siddhant Jayakumar · Razvan Pascanu · Peter E Latham · Yee Teh -
2021 Poster: Anti-Backdoor Learning: Training Clean Models on Poisoned Data »
Yige Li · Xixiang Lyu · Nodens Koren · Lingjuan Lyu · Bo Li · Xingjun Ma -
2021 Poster: Adversarial Attack Generation Empowered by Min-Max Optimization »
Jingkang Wang · Tianyun Zhang · Sijia Liu · Pin-Yu Chen · Jiacen Xu · Makan Fardad · Bo Li -
2021 : Reconnaissance Blind Chess + Q&A »
Ryan Gardner · Gino Perrotta · Corey Lowman · Casey Richardson · Andrew Newman · Jared Markowitz · Nathan Drenkow · Bart Paulhamus · Ashley J Llorens · Todd Neller · Raman Arora · Bo Li · Mykel J Kochenderfer -
2021 Poster: On Pathologies in KL-Regularized Reinforcement Learning from Expert Demonstrations »
Tim G. J. Rudner · Cong Lu · Michael A Osborne · Yarin Gal · Yee Teh -
2021 Poster: Vector-valued Gaussian Processes on Riemannian Manifolds via Gauge Independent Projected Kernels »
Michael Hutchinson · Alexander Terenin · Viacheslav Borovitskiy · So Takao · Yee Teh · Marc Deisenroth -
2021 Poster: TRS: Transferability Reduced Ensemble via Promoting Gradient Diversity and Model Smoothness »
Zhuolin Yang · Linyi Li · Xiaojun Xu · Shiliang Zuo · Qian Chen · Pan Zhou · Benjamin Rubinstein · Ce Zhang · Bo Li -
2021 Poster: BayesIMP: Uncertainty Quantification for Causal Data Fusion »
Siu Lun Chau · Jean-Francois Ton · Javier González · Yee Teh · Dino Sejdinovic -
2021 Poster: Neural Ensemble Search for Uncertainty Estimation and Dataset Shift »
Sheheryar Zaidi · Arber Zela · Thomas Elsken · Chris C Holmes · Frank Hutter · Yee Teh -
2020 : Francis Bach - Where is Machine Learning Going? »
Francis Bach -
2020 Workshop: Workshop on Dataset Curation and Security »
Nathalie Baracaldo · Yonatan Bisk · Avrim Blum · Michael Curry · John Dickerson · Micah Goldblum · Tom Goldstein · Bo Li · Avi Schwarzschild -
2020 Poster: Bayesian Deep Ensembles via the Neural Tangent Kernel »
Bobby He · Balaji Lakshminarayanan · Yee Whye Teh -
2020 Poster: Bootstrapping neural processes »
Juho Lee · Yoonho Lee · Jungtaek Kim · Eunho Yang · Sung Ju Hwang · Yee Whye Teh -
2020 Poster: Robust Deep Reinforcement Learning against Adversarial Perturbations on State Observations »
Huan Zhang · Hongge Chen · Chaowei Xiao · Bo Li · Mingyan Liu · Duane Boning · Cho-Jui Hsieh -
2020 Poster: How Robust are the Estimated Effects of Nonpharmaceutical Interventions against COVID-19? »
Mrinank Sharma · Sören Mindermann · Jan Brauner · Gavin Leech · Anna Stephenson · Tomáš Gavenčiak · Jan Kulveit · Yee Whye Teh · Leonid Chindelevitch · Yarin Gal -
2020 Spotlight: How Robust are the Estimated Effects of Nonpharmaceutical Interventions against COVID-19? »
Mrinank Sharma · Sören Mindermann · Jan Brauner · Gavin Leech · Anna Stephenson · Tomáš Gavenčiak · Jan Kulveit · Yee Whye Teh · Leonid Chindelevitch · Yarin Gal -
2020 Spotlight: Robust Deep Reinforcement Learning against Adversarial Perturbations on State Observations »
Huan Zhang · Hongge Chen · Chaowei Xiao · Bo Li · Mingyan Liu · Duane Boning · Cho-Jui Hsieh -
2020 Poster: On Convergence of Nearest Neighbor Classifiers over Feature Transformations »
Luka Rimanic · Cedric Renggli · Bo Li · Ce Zhang -
2019 : Coffee Break & Poster Session 2 »
Juho Lee · Yoonho Lee · Yee Whye Teh · Raymond A. Yeh · Yuan-Ting Hu · Alex Schwing · Sara Ahmadian · Alessandro Epasto · Marina Knittel · Ravi Kumar · Mohammad Mahdian · Christian Bueno · Aditya Sanghi · Pradeep Kumar Jayaraman · Ignacio Arroyo-Fernández · Andrew Hryniowski · Vinayak Mathur · Sanjay Singh · Shahrzad Haddadan · Vasco Portilheiro · Luna Zhang · Mert Yuksekgonul · Jhosimar Arias Figueroa · Deepak Maurya · Balaraman Ravindran · Frank NIELSEN · Philip Pham · Justin Payan · Andrew McCallum · Jinesh Mehta · Ke SUN -
2019 : Contributed Talk - Towards deep amortized clustering »
Juho Lee · Yoonho Lee · Yee Whye Teh -
2019 Poster: Elliptical Perturbations for Differential Privacy »
Matthew Reimherr · Jordan Awan -
2019 Poster: Stacked Capsule Autoencoders »
Adam Kosiorek · Sara Sabour · Yee Whye Teh · Geoffrey E Hinton -
2019 Poster: KNG: The K-Norm Gradient Mechanism »
Matthew Reimherr · Jordan Awan -
2019 Poster: Learning Perceptual Inference by Contrasting »
Chi Zhang · Baoxiong Jia · Feng Gao · Yixin Zhu · HongJing Lu · Song-Chun Zhu -
2019 Spotlight: Learning Perceptual Inference by Contrasting »
Chi Zhang · Baoxiong Jia · Feng Gao · Yixin Zhu · HongJing Lu · Song-Chun Zhu -
2019 Poster: Continual Unsupervised Representation Learning »
Dushyant Rao · Francesco Visin · Andrei A Rusu · Razvan Pascanu · Yee Whye Teh · Raia Hadsell -
2019 Poster: Effective End-to-end Unsupervised Outlier Detection via Inlier Priority of Discriminative Network »
Siqi Wang · Yijie Zeng · Xinwang Liu · En Zhu · Jianping Yin · Chuanfu Xu · Marius Kloft -
2019 Poster: Random Tessellation Forests »
Shufei Ge · Shijia Wang · Yee Whye Teh · Liangliang Wang · Lloyd Elliott -
2019 Poster: Variational Bayesian Optimal Experimental Design »
Adam Foster · Martin Jankowiak · Elias Bingham · Paul Horsfall · Yee Whye Teh · Thomas Rainforth · Noah Goodman -
2019 Spotlight: Variational Bayesian Optimal Experimental Design »
Adam Foster · Martin Jankowiak · Elias Bingham · Paul Horsfall · Yee Whye Teh · Thomas Rainforth · Noah Goodman -
2019 Poster: Augmented Neural ODEs »
Emilien Dupont · Arnaud Doucet · Yee Whye Teh -
2019 Poster: Continuous Hierarchical Representations with Poincaré Variational Auto-Encoders »
Emile Mathieu · Charline Le Lan · Chris Maddison · Ryota Tomioka · Yee Whye Teh -
2019 Tutorial: Representation Learning and Fairness »
Moustapha Cisse · Sanmi Koyejo -
2018 Workshop: AI for social good »
Margaux Luck · Tristan Sylvain · Joseph Paul Cohen · Arsene Fansi Tchango · Valentine Goddard · Aurelie Helouis · Yoshua Bengio · Sam Greydanus · Cody Wild · Taras Kucherenko · Arya Farahi · Jonathan Penn · Sean McGregor · Mark Crowley · Abhishek Gupta · Kenny Chen · Myriam Côté · Rediet Abebe -
2018 : Introduction of the workshop »
Razvan Pascanu · Yee Teh · Mark Ring · Marc Pickett -
2018 Workshop: Continual Learning »
Razvan Pascanu · Yee Teh · Marc Pickett · Mark Ring -
2018 Workshop: Critiquing and Correcting Trends in Machine Learning »
Thomas Rainforth · Matt Kusner · Benjamin Bloem-Reddy · Brooks Paige · Rich Caruana · Yee Whye Teh -
2018 Poster: Faithful Inversion of Generative Models for Effective Amortized Inference »
Stefan Webb · Adam Golinski · Rob Zinkov · Siddharth N · Thomas Rainforth · Yee Whye Teh · Frank Wood -
2018 Poster: Causal Inference via Kernel Deviance Measures »
Jovana Mitrovic · Dino Sejdinovic · Yee Whye Teh -
2018 Spotlight: Causal Inference via Kernel Deviance Measures »
Jovana Mitrovic · Dino Sejdinovic · Yee Whye Teh -
2018 Poster: Stochastic Expectation Maximization with Variance Reduction »
Jianfei Chen · Jun Zhu · Yee Whye Teh · Tong Zhang -
2018 Poster: Differentially Private Uniformly Most Powerful Tests for Binomial Data »
Jordan Awan · Aleksandra Slavković -
2018 Poster: Hamiltonian Variational Auto-Encoder »
Anthony Caterini · Arnaud Doucet · Dino Sejdinovic -
2018 Poster: Sequential Attend, Infer, Repeat: Generative Modelling of Moving Objects »
Adam Kosiorek · Hyunjik Kim · Yee Whye Teh · Ingmar Posner -
2018 Spotlight: Sequential Attend, Infer, Repeat: Generative Modelling of Moving Objects »
Adam Kosiorek · Hyunjik Kim · Yee Whye Teh · Ingmar Posner -
2018 Poster: Modelling sparsity, heterogeneity, reciprocity and community structure in temporal interaction data »
Xenia Miscouridou · Francois Caron · Yee Whye Teh -
2017 : Concluding remarks »
Francis Bach · Benjamin Guedj · Pascal Germain -
2017 : Neil Lawrence, Francis Bach and François Laviolette »
Neil Lawrence · Francis Bach · Francois Laviolette -
2017 : Sharp asymptotic and finite-sample rates of convergence of empirical measures in Wasserstein distance »
Francis Bach -
2017 : Panel Session »
Neil Lawrence · Finale Doshi-Velez · Zoubin Ghahramani · Yann LeCun · Max Welling · Yee Whye Teh · Ole Winther -
2017 : Overture »
Benjamin Guedj · Francis Bach · Pascal Germain -
2017 Workshop: (Almost) 50 shades of Bayesian Learning: PAC-Bayesian trends and insights »
Benjamin Guedj · Pascal Germain · Francis Bach -
2017 Invited Talk: On Bayesian Deep Learning and Deep Bayesian Learning »
Yee Whye Teh -
2017 Poster: On Structured Prediction Theory with Calibrated Convex Surrogate Losses »
Anton Osokin · Francis Bach · Simon Lacoste-Julien -
2017 Poster: Distral: Robust multitask reinforcement learning »
Yee Teh · Victor Bapst · Wojciech Czarnecki · John Quan · James Kirkpatrick · Raia Hadsell · Nicolas Heess · Razvan Pascanu -
2017 Oral: On Structured Prediction Theory with Calibrated Convex Surrogate Losses »
Anton Osokin · Francis Bach · Simon Lacoste-Julien -
2017 Poster: Nonlinear Acceleration of Stochastic Algorithms »
Damien Scieur · Francis Bach · Alexandre d'Aspremont -
2017 Poster: Filtering Variational Objectives »
Chris Maddison · John Lawson · George Tucker · Nicolas Heess · Mohammad Norouzi · Andriy Mnih · Arnaud Doucet · Yee Teh -
2017 Poster: Integration Methods and Optimization Algorithms »
Damien Scieur · Vincent Roulet · Francis Bach · Alexandre d'Aspremont -
2017 Poster: Collapsed variational Bayes for Markov jump processes »
Boqian Zhang · Jiangwei Pan · Vinayak Rao -
2017 Poster: Clone MCMC: Parallel High-Dimensional Gaussian Gibbs Sampling »
Andrei-Cristian Barbos · Francois Caron · Jean-François Giovannelli · Arnaud Doucet -
2016 : Francis Bach. Harder, Better, Faster, Stronger Convergence Rates for Least-Squares Regression. »
Francis Bach -
2016 : Submodular Functions: from Discrete to Continuous Domains »
Francis Bach -
2016 Poster: Gaussian Processes for Survival Analysis »
Tamara Fernandez · Nicolas Rivera · Yee Whye Teh -
2016 Tutorial: Large-Scale Optimization: Beyond Stochastic Gradient Descent and Convexity »
Suvrit Sra · Francis Bach -
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: A hybrid sampler for Poisson-Kingman mixture models »
Maria Lomeli · Stefano Favaro · Yee Whye Teh -
2015 Poster: Expectation Particle Belief Propagation »
Thibaut Lienart · Yee Whye Teh · Arnaud Doucet -
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 Oral: Asynchronous Anytime Sequential Monte Carlo »
Brooks Paige · Frank Wood · Arnaud Doucet · Yee Whye Teh -
2014 Poster: Mondrian Forests: Efficient Online Random Forests »
Balaji Lakshminarayanan · Daniel Roy · Yee Whye Teh -
2013 Poster: Learning with Invariance via Linear Functionals on Reproducing Kernel Hilbert Space »
Xinhua Zhang · Wee Sun Lee · Yee Whye Teh -
2013 Spotlight: Learning with Invariance via Linear Functionals on Reproducing Kernel Hilbert Space »
Xinhua Zhang · Wee Sun Lee · Yee Whye Teh -
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 -
2012 Poster: Searching for objects driven by context »
Bogdan Alexe · Nicolas Heess · Yee Whye Teh · Vittorio Ferrari -
2012 Poster: Learning Label Trees for Probabilistic Modelling of Implicit Feedback »
Andriy Mnih · Yee Whye Teh -
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: Repulsive Mixtures »
FRANCESCA PETRALIA · Vinayak Rao · David B Dunson -
2012 Spotlight: Searching for objects driven by context »
Bogdan Alexe · Nicolas Heess · Yee Whye Teh · Vittorio Ferrari -
2012 Poster: Scalable imputation of genetic data with a discrete fragmentation-coagulation process »
Lloyd T Elliott · Yee Whye Teh -
2011 Poster: Modelling Genetic Variations using Fragmentation-Coagulation Processes »
Yee Whye Teh · Charles Blundell · Lloyd T Elliott -
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 Tutorial: Modern Bayesian Nonparametrics »
Peter Orbanz · Yee Whye Teh -
2010 Poster: Improvements to the Sequence Memoizer »
Jan Gasthaus · Yee Whye Teh -
2009 Workshop: Nonparametric Bayes »
Dilan Gorur · Francois Caron · Yee Whye Teh · David B Dunson · Zoubin Ghahramani · 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: Bayesian Nonparametric Models on Decomposable Graphs »
Francois Caron · Arnaud Doucet -
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 Spotlight: Spatial Normalized Gamma Processes »
Vinayak Rao · Yee Whye Teh -
2009 Tutorial: Sequential Monte-Carlo Methods »
Arnaud Doucet · Nando de Freitas -
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: The Mondrian Process »
Daniel Roy · Yee Whye Teh -
2008 Spotlight: The Infinite Factorial Hidden Markov Model »
Jurgen Van Gael · Yee Whye Teh · Zoubin Ghahramani -
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: Dependent Dirichlet Process Spike Sorting »
Jan Gasthaus · Frank Wood · Dilan Gorur · Yee Whye Teh -
2008 Poster: An Efficient Sequential Monte Carlo Algorithm for Coalescent Clustering »
Dilan Gorur · Yee Whye Teh -
2007 Spotlight: Retrieved context and the discovery of semantic structure »
Vinayak Rao · Marc Howard -
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: Retrieved context and the discovery of semantic structure »
Vinayak Rao · Marc Howard -
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 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: Collapsed Variational Inference for HDP »
Yee Whye Teh · Kenichi Kurihara · Max Welling -
2007 Spotlight: Bayesian Policy Learning with Trans-Dimensional MCMC »
Matthew Hoffman · Arnaud Doucet · Nando de Freitas · Ajay Jasra -
2007 Poster: Bayesian Policy Learning with Trans-Dimensional MCMC »
Matthew Hoffman · Arnaud Doucet · Nando de Freitas · Ajay Jasra -
2006 Poster: A Collapsed Variational Bayesian Inference Algorithm for Latent Dirichlet Allocation »
Yee Whye Teh · David Newman · Max Welling