Timezone: »
We propose a distributed Markov chain Monte Carlo (MCMC) inference algorithm for large scale Bayesian posterior simulation. We assume that the dataset is partitioned and stored across nodes of a cluster. Our procedure involves an independent MCMC posterior sampler at each node based on its local partition of the data. Moment statistics of the local posteriors are collected from each sampler and propagated across the cluster using expectation propagation message passing with low communication costs. The moment sharing scheme improves posterior estimation quality by enforcing agreement among the samplers. We demonstrate the speed and inference quality of our method with empirical studies on Bayesian logistic regression and sparse linear regression with a spike-and-slab prior.
Author Information
Minjie Xu (Bloomberg LP)
Balaji Lakshminarayanan (Google Brain)
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.
Jun Zhu (Tsinghua University)
Bo Zhang (Tsinghua University)
More from the Same Authors
-
2021 : Understanding and Improving Robustness of VisionTransformers through patch-based NegativeAugmentation »
Yao Qin · Chiyuan Zhang · Ting Chen · Balaji Lakshminarayanan · Alex Beutel · Xuezhi Wang -
2021 : BEDS-Bench: Behavior of EHR-models under Distributional Shift - A Benchmark »
Anand Avati · Martin Seneviratne · Yuan Xue · Zhen Xu · Balaji Lakshminarayanan · Andrew Dai -
2021 : Reliable Graph Neural Networks for Drug Discovery Under Distributional Shift »
Kehang Han · Balaji Lakshminarayanan · Jeremiah Liu -
2021 : Uncertainty Quantification in End-to-End Implicit Neural Representations for Medical Imaging »
Bobby He · Francisca Vasconcelos · Yee Whye Teh -
2021 : Uncertainty Baselines: Benchmarks for Uncertainty & Robustness in Deep Learning »
Zachary Nado · Neil Band · Mark Collier · Josip Djolonga · Mike Dusenberry · Sebastian Farquhar · Qixuan Feng · Angelos Filos · Marton Havasi · Rodolphe Jenatton · Ghassen Jerfel · Jeremiah Liu · Zelda Mariet · Jeremy Nixon · Shreyas Padhy · Jie Ren · Tim G. J. Rudner · Yeming Wen · Florian Wenzel · Kevin Murphy · D. Sculley · Balaji Lakshminarayanan · Jasper Snoek · Yarin Gal · Dustin Tran -
2021 : Uncertainty Quantification in End-to-End Implicit Neural Representations for Medical Imaging »
Francisca Vasconcelos · Bobby He · Yee Teh -
2021 : Deep Classifiers with Label Noise Modeling and Distance Awareness »
Vincent Fortuin · Mark Collier · Florian Wenzel · James Allingham · Jeremiah Liu · Dustin Tran · Balaji Lakshminarayanan · Jesse Berent · Rodolphe Jenatton · Effrosyni Kokiopoulou -
2021 : Counter-Strike Deathmatch with Large-Scale Behavioural Cloning »
Tim Pearce · Jun Zhu -
2022 Poster: A Unified Hard-Constraint Framework for Solving Geometrically Complex PDEs »
Songming Liu · Hao Zhongkai · Chengyang Ying · Hang Su · Jun Zhu · Ze Cheng -
2022 Poster: Isometric 3D Adversarial Examples in the Physical World »
Yibo Miao · Yinpeng Dong · Jun Zhu · Xiao-Shan Gao -
2022 Poster: Confidence-based Reliable Learning under Dual Noises »
Peng Cui · Yang Yue · Zhijie Deng · Jun Zhu -
2022 Poster: EGSDE: Unpaired Image-to-Image Translation via Energy-Guided Stochastic Differential Equations »
Min Zhao · Fan Bao · Chongxuan LI · Jun Zhu -
2022 Poster: ViewFool: Evaluating the Robustness of Visual Recognition to Adversarial Viewpoints »
Yinpeng Dong · Shouwei Ruan · Hang Su · Caixin Kang · Xingxing Wei · Jun Zhu -
2022 Poster: DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Steps »
Cheng Lu · Yuhao Zhou · Fan Bao · Jianfei Chen · Chongxuan LI · Jun Zhu -
2022 Poster: Accelerated Linearized Laplace Approximation for Bayesian Deep Learning »
Zhijie Deng · Feng Zhou · Jun Zhu -
2022 : Pre-training via Denoising for Molecular Property Prediction »
Sheheryar Zaidi · Michael Schaarschmidt · James Martens · Hyunjik Kim · Yee Whye Teh · Alvaro Sanchez Gonzalez · Peter Battaglia · Razvan Pascanu · Jonathan Godwin -
2022 : Physics-Guided Discovery of Highly Nonlinear Parametric Partial Differential Equations »
Yingtao Luo · Qiang Liu · Yuntian Chen · Wenbo Hu · TIAN TIAN · Jun Zhu -
2022 : Out-of-Distribution Detection and Selective Generation for Conditional Language Models »
Jie Ren · Jiaming Luo · Yao Zhao · Kundan Krishna · Mohammad Saleh · Balaji Lakshminarayanan · Peter Liu -
2022 : Reliability benchmarks for image segmentation »
Estefany Kelly Buchanan · Michael Dusenberry · Jie Ren · Kevin Murphy · Balaji Lakshminarayanan · Dustin Tran -
2022 : Pushing the Accuracy-Fairness Tradeoff Frontier with Introspective Self-play »
Jeremiah Liu · Krishnamurthy Dvijotham · Jihyeon Lee · Quan Yuan · Martin Strobel · Balaji Lakshminarayanan · Deepak Ramachandran -
2022 : All are Worth Words: a ViT Backbone for Score-based Diffusion Models »
Fan Bao · Chongxuan LI · Yue Cao · Jun Zhu -
2022 : Why Are Conditional Generative Models Better Than Unconditional Ones? »
Fan Bao · Chongxuan LI · Jiacheng Sun · Jun Zhu -
2022 : On Equivalences between Weight and Function-Space Langevin Dynamics »
Ziyu Wang · Yuhao Zhou · Ruqi Zhang · Jun Zhu -
2022 : When Does Re-initialization Work? »
Sheheryar Zaidi · Tudor Berariu · Hyunjik Kim · Jorg Bornschein · Claudia Clopath · Yee Whye Teh · Razvan Pascanu -
2022 : Improving Zero-shot Generalization and Robustness of Multi-modal Models »
Yunhao Ge · Jie Ren · Ming-Hsuan Yang · Yuxiao Wang · Andrew Gallagher · Hartwig Adam · Laurent Itti · Balaji Lakshminarayanan · Jiaping Zhao -
2022 : Improving the Robustness of Conditional Language Models by Detecting and Removing Input Noise »
Kundan Krishna · Yao Zhao · Jie Ren · Balaji Lakshminarayanan · Jiaming Luo · Mohammad Saleh · Peter Liu -
2023 Poster: ProlificDreamer: High-Fidelity and Diverse Text-to-3D Generation with Variational Score Distillation »
Zhengyi Wang · Cheng Lu · Yikai Wang · Fan Bao · Chongxuan LI · Hang Su · Jun Zhu -
2023 Poster: Memory Efficient Optimizers with 4-bit States »
Bingrui Li · Jianfei Chen · Jun Zhu -
2023 Poster: Overcoming Recency Bias of Normalization Statistics in Continual Learning: Balance and Adaptation »
Yilin Lyu · Liyuan Wang · Xingxing Zhang · Zicheng Sun · Hang Su · Jun Zhu · Liping Jing -
2023 Poster: Geometric Neural Diffusion Processes »
Emile Mathieu · Vincent Dutordoir · Michael Hutchinson · Valentin De Bortoli · Yee Whye Teh · Richard Turner -
2023 Poster: Deep Stochastic Processes via Functional Markov Transition Operators »
Jin Xu · Emilien Dupont · Kaspar Märtens · Thomas Rainforth · Yee Whye Teh -
2023 Poster: Diffusion Models and Semi-Supervised Learners Benefit Mutually with Few Labels »
Zebin You · Yong Zhong · Fan Bao · Jiacheng Sun · Chongxuan LI · Jun Zhu -
2023 Poster: Training Transformers with 4-bit Integers »
Haocheng Xi · ChangHao Li · Jianfei Chen · Jun Zhu -
2023 Poster: DPM-Solver-v3: Improved Diffusion ODE Solvers with Empirical Model Statistics »
Kaiwen Zheng · Cheng Lu · Jianfei Chen · Jun Zhu -
2023 Poster: Hierarchical Decomposition of Prompt-Based Continual Learning: Rethinking Obscured Sub-optimality »
Liyuan Wang · Jingyi Xie · Xingxing Zhang · Mingyi Huang · Hang Su · Jun Zhu -
2023 Poster: Synthetic Experience Replay »
Cong Lu · Philip Ball · Yee Whye Teh · Jack Parker-Holder -
2023 Poster: Learning Sample Difficulty from Pre-trained Models for Reliable Prediction »
Peng Cui · Dan Zhang · Zhijie Deng · Yinpeng Dong · Jun Zhu -
2023 Poster: Towards Accelerated Model Training via Bayesian Data Selection »
Zhijie Deng · Peng Cui · Jun Zhu -
2022 Spotlight: Lightning Talks 6A-2 »
Yichuan Mo · Botao Yu · Gang Li · Zezhong Xu · Haoran Wei · Arsene Fansi Tchango · Raef Bassily · Haoyu Lu · Qi Zhang · Songming Liu · Mingyu Ding · Peiling Lu · Yifei Wang · Xiang Li · Dongxian Wu · Ping Guo · Wen Zhang · Hao Zhongkai · Mehryar Mohri · Rishab Goel · Yisen Wang · Yifei Wang · Yangguang Zhu · Zhi Wen · Ananda Theertha Suresh · Chengyang Ying · Yujie Wang · Peng Ye · Rui Wang · Nanyi Fei · Hui Chen · Yiwen Guo · Wei Hu · Chenglong Liu · Julien Martel · Yuqi Huo · Wu Yichao · Hang Su · Yisen Wang · Peng Wang · Huajun Chen · Xu Tan · Jun Zhu · Ding Liang · Zhiwu Lu · Joumana Ghosn · Shanshan Zhang · Wei Ye · Ze Cheng · Shikun Zhang · Tao Qin · Tie-Yan Liu -
2022 Spotlight: A Unified Hard-Constraint Framework for Solving Geometrically Complex PDEs »
Songming Liu · Hao Zhongkai · Chengyang Ying · Hang Su · Jun Zhu · Ze Cheng -
2022 Spotlight: EGSDE: Unpaired Image-to-Image Translation via Energy-Guided Stochastic Differential Equations »
Min Zhao · Fan Bao · Chongxuan LI · Jun Zhu -
2022 Spotlight: Accelerated Linearized Laplace Approximation for Bayesian Deep Learning »
Zhijie Deng · Feng Zhou · Jun Zhu -
2022 Spotlight: DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Steps »
Cheng Lu · Yuhao Zhou · Fan Bao · Jianfei Chen · Chongxuan LI · Jun Zhu -
2022 Spotlight: Lightning Talks 4B-1 »
Alexandra Senderovich · Zhijie Deng · Navid Ansari · Xuefei Ning · Yasmin Salehi · Xiang Huang · Chenyang Wu · Kelsey Allen · Jiaqi Han · Nikita Balagansky · Tatiana Lopez-Guevara · Tianci Li · Zhanhong Ye · Zixuan Zhou · Feng Zhou · Ekaterina Bulatova · Daniil Gavrilov · Wenbing Huang · Dennis Giannacopoulos · Hans-peter Seidel · Anton Obukhov · Kimberly Stachenfeld · Hongsheng Liu · Jun Zhu · Junbo Zhao · Hengbo Ma · Nima Vahidi Ferdowsi · Zongzhang Zhang · Vahid Babaei · Jiachen Li · Alvaro Sanchez Gonzalez · Yang Yu · Shi Ji · Maxim Rakhuba · Tianchen Zhao · Yiping Deng · Peter Battaglia · Josh Tenenbaum · Zidong Wang · Chuang Gan · Changcheng Tang · Jessica Hamrick · Kang Yang · Tobias Pfaff · Yang Li · Shuang Liang · Min Wang · Huazhong Yang · Haotian CHU · Yu Wang · Fan Yu · Bei Hua · Lei Chen · Bin Dong -
2022 Spotlight: Lightning Talks 3B-2 »
Yu Huang · Tero Karras · Maxim Kodryan · Shiau Hong Lim · Shudong Huang · Ziyu Wang · Siqiao Xue · ILYAS MALIK · Ekaterina Lobacheva · Miika Aittala · Hongjie Wu · Yuhao Zhou · Yingbin Liang · Xiaoming Shi · Jun Zhu · Maksim Nakhodnov · Timo Aila · Yazhou Ren · James Zhang · Longbo Huang · Dmitry Vetrov · Ivor Tsang · Hongyuan Mei · Samuli Laine · Zenglin Xu · Wentao Feng · Jiancheng Lv -
2022 Spotlight: Fast Instrument Learning with Faster Rates »
Ziyu Wang · Yuhao Zhou · Jun Zhu -
2022 Spotlight: Lightning Talks 1A-4 »
Siwei Wang · Jing Liu · Nianqiao Ju · Shiqian Li · Eloïse Berthier · Muhammad Faaiz Taufiq · Arsene Fansi Tchango · Chen Liang · Chulin Xie · Jordan Awan · Jean-Francois Ton · Ziad Kobeissi · Wenguan Wang · Xinwang Liu · Kewen Wu · Rishab Goel · Jiaxu Miao · Suyuan Liu · Julien Martel · Ruobin Gong · Francis Bach · Chi Zhang · Rob Cornish · Sanmi Koyejo · Zhi Wen · Yee Whye Teh · Yi Yang · Jiaqi Jin · Bo Li · Yixin Zhu · Vinayak Rao · Wenxuan Tu · Gaetan Marceau Caron · Arnaud Doucet · Xinzhong Zhu · Joumana Ghosn · En Zhu -
2022 Spotlight: Conformal Off-Policy Prediction in Contextual Bandits »
Muhammad Faaiz Taufiq · Jean-Francois Ton · Rob Cornish · Yee Whye Teh · Arnaud Doucet -
2022 : Out-of-Distribution Detection and Selective Generation for Conditional Language Models »
Jie Ren · Jiaming Luo · Yao Zhao · Kundan Krishna · Mohammad Saleh · Balaji Lakshminarayanan · Peter Liu -
2022 Poster: Tractable Function-Space Variational Inference in Bayesian Neural Networks »
Tim G. J. Rudner · Zonghao Chen · Yee Whye Teh · Yarin Gal -
2022 Poster: Conformal Off-Policy Prediction in Contextual Bandits »
Muhammad Faaiz Taufiq · Jean-Francois Ton · Rob Cornish · Yee Whye Teh · Arnaud Doucet -
2022 Poster: Riemannian Score-Based Generative Modelling »
Valentin De Bortoli · Emile Mathieu · Michael Hutchinson · James Thornton · Yee Whye Teh · Arnaud Doucet -
2022 Poster: Understanding and Improving Robustness of Vision Transformers through Patch-based Negative Augmentation »
Yao Qin · Chiyuan Zhang · Ting Chen · Balaji Lakshminarayanan · Alex Beutel · Xuezhi Wang -
2022 Poster: Fast Instrument Learning with Faster Rates »
Ziyu Wang · Yuhao Zhou · Jun Zhu -
2022 Poster: Censored Quantile Regression Neural Networks for Distribution-Free Survival Analysis »
Tim Pearce · Jong-Hyeon Jeong · yichen jia · Jun Zhu -
2021 Poster: On Contrastive Representations of Stochastic Processes »
Emile Mathieu · Adam Foster · Yee Teh -
2021 Poster: Exploring the Limits of Out-of-Distribution Detection »
Stanislav Fort · Jie Ren · Balaji Lakshminarayanan -
2021 Poster: Stability and Generalization of Bilevel Programming in Hyperparameter Optimization »
Fan Bao · Guoqiang Wu · Chongxuan LI · Jun Zhu · Bo Zhang -
2021 Poster: Group Equivariant Subsampling »
Jin Xu · Hyunjik Kim · Thomas Rainforth · Yee Teh -
2021 Poster: On the Convergence of Prior-Guided Zeroth-Order Optimization Algorithms »
Shuyu Cheng · Guoqiang Wu · Jun Zhu -
2021 Poster: Powerpropagation: A sparsity inducing weight reparameterisation »
Jonathan Richard Schwarz · Siddhant Jayakumar · Razvan Pascanu · Peter E Latham · Yee Teh -
2021 Poster: Scalable Quasi-Bayesian Inference for Instrumental Variable Regression »
Ziyu Wang · Yuhao Zhou · Tongzheng Ren · Jun Zhu -
2021 Poster: Soft Calibration Objectives for Neural Networks »
Archit Karandikar · Nicholas Cain · Dustin Tran · Balaji Lakshminarayanan · Jonathon Shlens · Michael Mozer · Becca Roelofs -
2021 Poster: Rethinking and Reweighting the Univariate Losses for Multi-Label Ranking: Consistency and Generalization »
Guoqiang Wu · Chongxuan LI · Kun Xu · Jun Zhu -
2021 Poster: AFEC: Active Forgetting of Negative Transfer in Continual Learning »
Liyuan Wang · Mingtian Zhang · Zhongfan Jia · Qian Li · Chenglong Bao · Kaisheng Ma · Jun Zhu · Yi Zhong -
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: Accumulative Poisoning Attacks on Real-time Data »
Tianyu Pang · Xiao Yang · Yinpeng Dong · Hang Su · Jun Zhu -
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: 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 Poster: Multi-label classification: do Hamming loss and subset accuracy really conflict with each other? »
Guoqiang Wu · Jun Zhu -
2020 Poster: Bi-level Score Matching for Learning Energy-based Latent Variable Models »
Fan Bao · Chongxuan LI · Kun Xu · Hang Su · Jun Zhu · Bo Zhang -
2020 Poster: Bayesian Deep Ensembles via the Neural Tangent Kernel »
Bobby He · Balaji Lakshminarayanan · Yee Whye Teh -
2020 Poster: Further Analysis of Outlier Detection with Deep Generative Models »
Ziyu Wang · Bin Dai · David P Wipf · Jun Zhu -
2020 Poster: Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awareness »
Jeremiah Liu · Zi Lin · Shreyas Padhy · Dustin Tran · Tania Bedrax Weiss · Balaji Lakshminarayanan -
2020 Poster: Efficient Learning of Generative Models via Finite-Difference Score Matching »
Tianyu Pang · Kun Xu · Chongxuan LI · Yang Song · Stefano Ermon · Jun Zhu -
2020 Poster: Bootstrapping neural processes »
Juho Lee · Yoonho Lee · Jungtaek Kim · Eunho Yang · Sung Ju Hwang · Yee Whye Teh -
2020 Poster: Calibrated Reliable Regression using Maximum Mean Discrepancy »
Peng Cui · Wenbo Hu · Jun Zhu -
2020 Tutorial: (Track2) Practical Uncertainty Estimation and Out-of-Distribution Robustness in Deep Learning Q&A »
Dustin Tran · Balaji Lakshminarayanan · Jasper Snoek -
2020 Poster: Boosting Adversarial Training with Hypersphere Embedding »
Tianyu Pang · Xiao Yang · Yinpeng Dong · Kun Xu · Jun Zhu · Hang Su -
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 Poster: Adversarial Distributional Training for Robust Deep Learning »
Yinpeng Dong · Zhijie Deng · Tianyu Pang · Jun Zhu · Hang Su -
2020 Poster: Understanding and Exploring the Network with Stochastic Architectures »
Zhijie Deng · Yinpeng Dong · Shifeng Zhang · Jun Zhu -
2020 Tutorial: (Track2) Practical Uncertainty Estimation and Out-of-Distribution Robustness in Deep Learning »
Dustin Tran · Balaji Lakshminarayanan · Jasper Snoek -
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: Stacked Capsule Autoencoders »
Adam Kosiorek · Sara Sabour · Yee Whye Teh · Geoffrey E Hinton -
2019 Poster: Improving Black-box Adversarial Attacks with a Transfer-based Prior »
Shuyu Cheng · Yinpeng Dong · Tianyu Pang · Hang Su · Jun Zhu -
2019 Poster: Continual Unsupervised Representation Learning »
Dushyant Rao · Francesco Visin · Andrei A Rusu · Razvan Pascanu · Yee Whye Teh · Raia Hadsell -
2019 Poster: Generative Well-intentioned Networks »
Justin Cosentino · Jun Zhu -
2019 Poster: Multi-objects Generation with Amortized Structural Regularization »
Kun Xu · Chongxuan LI · Jun Zhu · Bo Zhang -
2019 Poster: Random Tessellation Forests »
Shufei Ge · Shijia Wang · Yee Whye Teh · Liangliang Wang · Lloyd Elliott -
2019 Poster: Can you trust your model's uncertainty? Evaluating predictive uncertainty under dataset shift »
Jasper Snoek · Yaniv Ovadia · Emily Fertig · Balaji Lakshminarayanan · Sebastian Nowozin · D. Sculley · Joshua Dillon · Jie Ren · Zachary Nado -
2019 Poster: Likelihood Ratios for Out-of-Distribution Detection »
Jie Ren · Peter Liu · Emily Fertig · Jasper Snoek · Ryan Poplin · Mark Depristo · Joshua Dillon · Balaji Lakshminarayanan -
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 -
2018 : TBC 8 »
Balaji Lakshminarayanan -
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: Semi-crowdsourced Clustering with Deep Generative Models »
Yucen Luo · TIAN TIAN · Jiaxin Shi · Jun Zhu · Bo Zhang -
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: 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: Towards Robust Detection of Adversarial Examples »
Tianyu Pang · Chao Du · Yinpeng Dong · Jun Zhu -
2018 Spotlight: Towards Robust Detection of Adversarial Examples »
Tianyu Pang · Chao Du · Yinpeng Dong · Jun Zhu -
2018 Poster: Modelling sparsity, heterogeneity, reciprocity and community structure in temporal interaction data »
Xenia Miscouridou · Francois Caron · Yee Whye Teh -
2018 Poster: Graphical Generative Adversarial Networks »
Chongxuan LI · Max Welling · Jun Zhu · Bo Zhang -
2017 : Panel Session »
Neil Lawrence · Finale Doshi-Velez · Zoubin Ghahramani · Yann LeCun · Max Welling · Yee Whye Teh · Ole Winther -
2017 Invited Talk: On Bayesian Deep Learning and Deep Bayesian Learning »
Yee Whye Teh -
2017 Poster: Triple Generative Adversarial Nets »
Chongxuan LI · Kun Xu · Jun Zhu · Bo Zhang -
2017 Poster: Distral: Robust multitask reinforcement learning »
Yee Teh · Victor Bapst · Wojciech Czarnecki · John Quan · James Kirkpatrick · Raia Hadsell · Nicolas Heess · Razvan Pascanu -
2017 Poster: Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles »
Balaji Lakshminarayanan · Alexander Pritzel · Charles Blundell -
2017 Spotlight: Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles »
Balaji Lakshminarayanan · Alexander Pritzel · Charles Blundell -
2017 Poster: Filtering Variational Objectives »
Chris Maddison · John Lawson · George Tucker · Nicolas Heess · Mohammad Norouzi · Andriy Mnih · Arnaud Doucet · Yee Teh -
2017 Poster: Population Matching Discrepancy and Applications in Deep Learning »
Jianfei Chen · Chongxuan LI · Yizhong Ru · Jun Zhu -
2016 Poster: Kernel Bayesian Inference with Posterior Regularization »
Yang Song · Jun Zhu · Yong Ren -
2016 Poster: Gaussian Processes for Survival Analysis »
Tamara Fernandez · Nicolas Rivera · Yee Whye Teh -
2016 Poster: Stochastic Gradient Geodesic MCMC Methods »
Chang Liu · Jun Zhu · Yang Song -
2016 Poster: Conditional Generative Moment-Matching Networks »
Yong Ren · Jun Zhu · Jialian Li · Yucen Luo -
2015 : Mondrian Forests for Large-Scale regression when uncertainty matters »
Balaji Lakshminarayanan -
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: Max-Margin Majority Voting for Learning from Crowds »
TIAN TIAN · Jun Zhu -
2015 Poster: Max-Margin Deep Generative Models »
Chongxuan Li · Jun Zhu · Tim Shi · Bo Zhang -
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: Asynchronous Anytime Sequential Monte Carlo »
Brooks Paige · Frank Wood · Arnaud Doucet · Yee Whye Teh -
2014 Poster: Spectral Methods for Supervised Topic Models »
Yining Wang · Jun Zhu -
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 -
2014 Poster: Robust Bayesian Max-Margin Clustering »
Changyou Chen · Jun Zhu · Xinhua Zhang -
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 -
2013 Poster: Scalable Inference for Logistic-Normal Topic Models »
Jianfei Chen · Jun Zhu · Zi Wang · Xun Zheng · Bo Zhang -
2012 Poster: Searching for objects driven by context »
Bogdan Alexe · Nicolas Heess · Yee Whye Teh · Vittorio Ferrari -
2012 Poster: Monte Carlo Methods for Maximum Margin Supervised Topic Models »
Qixia Jiang · Jun Zhu · Maosong Sun · Eric Xing -
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 Spotlight: Searching for objects driven by context »
Bogdan Alexe · Nicolas Heess · Yee Whye Teh · Vittorio Ferrari -
2012 Poster: Bayesian Nonparametric Maximum Margin Matrix Factorization for Collaborative Prediction »
Minjie Xu · Jun Zhu · Bo Zhang -
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 Poster: Infinite Latent SVM for Classification and Multi-task Learning »
Jun Zhu · Ning Chen · Eric Xing -
2011 Tutorial: Modern Bayesian Nonparametrics »
Peter Orbanz · Yee Whye Teh -
2010 Poster: Large Margin Learning of Upstream Scene Understanding Models »
Jun Zhu · Li-Jia Li · Li Fei-Fei · Eric Xing -
2010 Poster: Predictive Subspace Learning for Multi-view Data: a Large Margin Approach »
Ning Chen · Jun Zhu · Eric Xing -
2010 Poster: Improvements to the Sequence Memoizer »
Jan Gasthaus · Yee Whye Teh -
2010 Poster: Adaptive Multi-Task Lasso: with Application to eQTL Detection »
Seunghak Lee · Jun Zhu · Eric Xing -
2010 Poster: Efficient Relational Learning with Hidden Variable Detection »
Ni Lao · Jun Zhu · Liu Xinwang · Yandong Liu · William Cohen -
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: 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 -
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: Partially Observed Maximum Entropy Discrimination Markov Networks »
Jun Zhu · Eric Xing · Bo Zhang -
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 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 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 -
2006 Poster: A Collapsed Variational Bayesian Inference Algorithm for Latent Dirichlet Allocation »
Yee Whye Teh · David Newman · Max Welling