Timezone: »
Projection robust Wasserstein (PRW) distance, or Wasserstein projection pursuit (WPP), is a robust variant of the Wasserstein distance. Recent work suggests that this quantity is more robust than the standard Wasserstein distance, in particular when comparing probability measures in high-dimensions. However, it is ruled out for practical application because the optimization model is essentially non-convex and non-smooth which makes the computation intractable. Our contribution in this paper is to revisit the original motivation behind WPP/PRW, but take the hard route of showing that, despite its non-convexity and lack of nonsmoothness, and even despite some hardness results proved by~\citet{Niles-2019-Estimation} in a minimax sense, the original formulation for PRW/WPP \textit{can} be efficiently computed in practice using Riemannian optimization, yielding in relevant cases better behavior than its convex relaxation. More specifically, we provide three simple algorithms with solid theoretical guarantee on their complexity bound (one in the appendix), and demonstrate their effectiveness and efficiency by conducing extensive experiments on synthetic and real data. This paper provides a first step into a computational theory of the PRW distance and provides the links between optimal transport and Riemannian optimization.
Author Information
Tianyi Lin (UC Berkeley)
Chenyou Fan (The Chinese University of Hong Kong, Shenzhen)
Nhat Ho (University of Texas at Austin)
Marco Cuturi (Google Brain & CREST - ENSAE)
Marco Cuturi is a research scientist at Apple, in Paris. He received his Ph.D. in 11/2005 from the Ecole des Mines de Paris in applied mathematics. Before that he graduated from National School of Statistics (ENSAE) with a master degree (MVA) from ENS Cachan. He worked as a post-doctoral researcher at the Institute of Statistical Mathematics, Tokyo, between 11/2005 and 3/2007 and then in the financial industry between 4/2007 and 9/2008. After working at the ORFE department of Princeton University as a lecturer between 2/2009 and 8/2010, he was at the Graduate School of Informatics of Kyoto University between 9/2010 and 9/2016 as a tenured associate professor. He joined ENSAE in 9/2016 as a professor, where he is now working part-time. He was at Google between 10/2018 and 1/2022. His main employment is now with Apple, since 1/2022, as a research scientist working on fundamental aspects of machine learning.
Michael Jordan (UC Berkeley)
Related Events (a corresponding poster, oral, or spotlight)
-
2020 Spotlight: Projection Robust Wasserstein Distance and Riemannian Optimization »
Fri. Dec 11th 03:50 -- 04:00 AM Room Orals & Spotlights: Optimization
More from the Same Authors
-
2021 Spotlight: Learning Equilibria in Matching Markets from Bandit Feedback »
Meena Jagadeesan · Alexander Wei · Yixin Wang · Michael Jordan · Jacob Steinhardt -
2021 Spotlight: Robust Learning of Optimal Auctions »
Wenshuo Guo · Michael Jordan · Emmanouil Zampetakis -
2021 : Optimization with Adaptive Step Size Selection from a Dynamical Systems Perspective »
Neha Wadia · Michael Jordan · Michael Muehlebach -
2021 : Optimization with Adaptive Step Size Selection from a Dynamical Systems Perspective »
Neha Wadia · Michael Jordan · Michael Muehlebach -
2021 : Last-Iterate Convergence of Saddle Point Optimizers via High-Resolution Differential Equations »
Tatjana Chavdarova · Michael Jordan · Emmanouil Zampetakis -
2021 : On the convergence of stochastic extragradient for bilinear games using restarted iteration averaging »
Chris Junchi Li · Yaodong Yu · Nicolas Loizou · Gauthier Gidel · Yi Ma · Nicolas Le Roux perso · Michael Jordan -
2021 : On the convergence of stochastic extragradient for bilinear games using restarted iteration averaging »
Chris Junchi Li · Yaodong Yu · Nicolas Loizou · Gauthier Gidel · Yi Ma · Nicolas Le Roux perso · Michael Jordan -
2021 : GPU-Podracer: Scalable and Elastic Library for Cloud-Native Deep Reinforcement Learning »
Xiao-Yang Liu · Zhuoran Yang · Zhaoran Wang · Anwar Walid · Jian Guo · Michael Jordan -
2021 : Learning Two-Player Mixture Markov Games: Kernel Function Approximation and Correlated Equilibrium »
Chris Junchi Li · Dongruo Zhou · Quanquan Gu · Michael Jordan -
2021 : Desiderata for Representation Learning: A Causal Perspective »
Yixin Wang · Michael Jordan -
2021 : Linear-Time Gromov Wasserstein Distances using Low Rank Couplings and Costs »
Meyer Scetbon · Gabriel Peyré · Marco Cuturi -
2021 : Linear-Time Gromov Wasserstein Distances using Low Rank Couplings and Costs »
Meyer Scetbon · Gabriel Peyré · Marco Cuturi -
2022 Poster: Rank Diminishing in Deep Neural Networks »
Ruili Feng · Kecheng Zheng · Yukun Huang · Deli Zhao · Michael Jordan · Zheng-Jun Zha -
2022 : Nesterov Meets Optimism: Rate-Optimal Optimistic-Gradient-Based Method for Stochastic Bilinearly-Coupled Minimax Optimization »
Chris Junchi Li · Angela Yuan · Gauthier Gidel · Michael Jordan -
2022 : Solving Constrained Variational Inequalities via a First-order Interior Point-based Method »
Tong Yang · Michael Jordan · Tatjana Chavdarova -
2022 : Perseus: A Simple and Optimal High-Order Method for Variational Inequalities »
Tianyi Lin · Michael Jordan -
2022 : Statistical and Computational Complexities of BFGS Quasi-Newton Method for Generalized Linear Models »
Qiujiang Jin · Aryan Mokhtari · Nhat Ho · Tongzheng Ren -
2022 : Towards Provably Personalized Federated Learning via Threshold-Clustering of Similar Clients »
Mariel A Werner · Lie He · Sai Praneeth Karimireddy · Michael Jordan · Martin Jaggi -
2022 : Valid Inference after Causal Discovery »
Paula Gradu · Tijana Zrnic · Yixin Wang · Michael Jordan -
2022 : A General Framework for Sample-Efficient Function Approximation in Reinforcement Learning »
Zixiang Chen · Chris Junchi Li · Angela Yuan · Quanquan Gu · Michael Jordan -
2023 Poster: A Unifying Perspective on Multi-Calibration: Game Dynamics for Multi-Objective Learning »
Nika Haghtalab · Michael Jordan · Eric Zhao -
2023 Poster: LVM-Med: Learning Large-Scale Self-Supervised Vision Models for Medical Imaging via Second-order Graph Matching »
Duy Minh Ho Nguyen · Hoang Nguyen · Nghiem Diep · Tan Ngoc Pham · Tri Cao · Binh Nguyen · Paul Swoboda · Nhat Ho · Shadi Albarqouni · Pengtao Xie · Mathias Niepert · Daniel Sonntag -
2023 Poster: Minimax Optimal Rate for Parameter Estimation in Multivariate Deviated Models »
Dat Do · Huy Nguyen · Khai Nguyen · Nhat Ho -
2023 Poster: Energy-Based Sliced Wasserstein Distance »
Khai Nguyen · Nhat Ho -
2023 Poster: Class-conditional conformal prediction with many classes »
Tiffany Ding · Anastasios Angelopoulos · Stephen Bates · Michael Jordan · Ryan Tibshirani -
2023 Poster: On Learning Necessary and Sufficient Causal Graphs »
Hengrui Cai · Yixin Wang · Michael Jordan · Rui Song -
2023 Poster: Towards Optimal Caching and Model Selection for Large Model Inference »
Banghua Zhu · Ying Sheng · Lianmin Zheng · Clark Barrett · Michael Jordan · Jiantao Jiao -
2023 Poster: Doubly-Robust Self-Training »
Banghua Zhu · Mingyu Ding · Philip Jacobson · Ming Wu · Wei Zhan · Michael Jordan · Jiantao Jiao -
2023 Poster: Unbalanced Low-rank Optimal Transport Solvers »
Meyer Scetbon · Michal Klein · Giovanni Palla · Marco Cuturi -
2023 Poster: Demystifying Softmax Gating in Gaussian Mixture of Experts »
Huy Nguyen · TrungTin Nguyen · Nhat Ho -
2023 Poster: Optimal Extragradient-Based Algorithms for Stochastic Variational Inequalities with Separable Structure »
Angela Yuan · Chris Junchi Li · Gauthier Gidel · Michael Jordan · Quanquan Gu · Simon Du -
2023 Poster: Improved Bayes Risk Can Yield Reduced Social Welfare Under Competition »
Meena Jagadeesan · Michael Jordan · Jacob Steinhardt · Nika Haghtalab -
2023 Poster: Markovian Sliced Wasserstein Distances: Beyond Independent Projections »
Khai Nguyen · Tongzheng Ren · Nhat Ho -
2023 Poster: Designing Robust Transformers using Robust Kernel Density Estimation »
Xing Han · Tongzheng Ren · Tan Nguyen · Khai Nguyen · Joydeep Ghosh · Nhat Ho -
2023 Workshop: Optimal Transport and Machine Learning »
Anna Korba · Aram-Alexandre Pooladian · Charlotte Bunne · David Alvarez-Melis · Marco Cuturi · Ziv Goldfeld -
2022 : Mechanisms that Incentivize Data Sharing in Federated Learning »
Sai Praneeth Karimireddy · Wenshuo Guo · Michael Jordan -
2022 Poster: Supervised Training of Conditional Monge Maps »
Charlotte Bunne · Andreas Krause · Marco Cuturi -
2022 Poster: Off-Policy Evaluation with Policy-Dependent Optimization Response »
Wenshuo Guo · Michael Jordan · Angela Zhou -
2022 Poster: First-Order Algorithms for Min-Max Optimization in Geodesic Metric Spaces »
Michael Jordan · Tianyi Lin · Emmanouil-Vasileios Vlatakis-Gkaragkounis -
2022 Poster: Amortized Projection Optimization for Sliced Wasserstein Generative Models »
Khai Nguyen · Nhat Ho -
2022 Poster: Efficient and Modular Implicit Differentiation »
Mathieu Blondel · Quentin Berthet · Marco Cuturi · Roy Frostig · Stephan Hoyer · Felipe Llinares-Lopez · Fabian Pedregosa · Jean-Philippe Vert -
2022 Poster: Revisiting Sliced Wasserstein on Images: From Vectorization to Convolution »
Khai Nguyen · Nhat Ho -
2022 Poster: Learning Two-Player Markov Games: Neural Function Approximation and Correlated Equilibrium »
Chris Junchi Li · Dongruo Zhou · Quanquan Gu · Michael Jordan -
2022 Poster: Learn to Match with No Regret: Reinforcement Learning in Markov Matching Markets »
Yifei Min · Tianhao Wang · Ruitu Xu · Zhaoran Wang · Michael Jordan · Zhuoran Yang -
2022 Poster: Stochastic Multiple Target Sampling Gradient Descent »
Hoang Phan · Ngoc Tran · Trung Le · Toan Tran · Nhat Ho · Dinh Phung -
2022 Poster: Robust Calibration with Multi-domain Temperature Scaling »
Yaodong Yu · Stephen Bates · Yi Ma · Michael Jordan -
2022 Poster: On-Demand Sampling: Learning Optimally from Multiple Distributions »
Nika Haghtalab · Michael Jordan · Eric Zhao -
2022 Poster: Beyond black box densities: Parameter learning for the deviated components »
Dat Do · Nhat Ho · XuanLong Nguyen -
2022 Poster: Gradient-Free Methods for Deterministic and Stochastic Nonsmooth Nonconvex Optimization »
Tianyi Lin · Zeyu Zheng · Michael Jordan -
2022 Poster: FourierFormer: Transformer Meets Generalized Fourier Integral Theorem »
Tan Nguyen · Minh Pham · Tam Nguyen · Khai Nguyen · Stanley Osher · Nhat Ho -
2022 Poster: Improving Transformer with an Admixture of Attention Heads »
Tan Nguyen · Tam Nguyen · Hai Do · Khai Nguyen · Vishwanath Saragadam · Minh Pham · Khuong Duy Nguyen · Nhat Ho · Stanley Osher -
2022 Poster: TCT: Convexifying Federated Learning using Bootstrapped Neural Tangent Kernels »
Yaodong Yu · Alexander Wei · Sai Praneeth Karimireddy · Yi Ma · Michael Jordan -
2022 Poster: Empirical Gateaux Derivatives for Causal Inference »
Michael Jordan · Yixin Wang · Angela Zhou -
2022 Poster: Low-rank Optimal Transport: Approximation, Statistics and Debiasing »
Meyer Scetbon · Marco Cuturi -
2021 Workshop: Optimal Transport and Machine Learning »
Jason Altschuler · Charlotte Bunne · Laetitia Chapel · Marco Cuturi · Rémi Flamary · Gabriel Peyré · Alexandra Suvorikova -
2021 Poster: Robust Learning of Optimal Auctions »
Wenshuo Guo · Michael Jordan · Emmanouil Zampetakis -
2021 Poster: Learning in Multi-Stage Decentralized Matching Markets »
Xiaowu Dai · Michael Jordan -
2021 Poster: Who Leads and Who Follows in Strategic Classification? »
Tijana Zrnic · Eric Mazumdar · Shankar Sastry · Michael Jordan -
2021 Poster: Test-time Collective Prediction »
Celestine Mendler-Dünner · Wenshuo Guo · Stephen Bates · Michael Jordan -
2021 Poster: On the Theory of Reinforcement Learning with Once-per-Episode Feedback »
Niladri Chatterji · Aldo Pacchiano · Peter Bartlett · Michael Jordan -
2021 Poster: Wasserstein Flow Meets Replicator Dynamics: A Mean-Field Analysis of Representation Learning in Actor-Critic »
Yufeng Zhang · Siyu Chen · Zhuoran Yang · Michael Jordan · Zhaoran Wang -
2021 Poster: Tactical Optimism and Pessimism for Deep Reinforcement Learning »
Ted Moskovitz · Jack Parker-Holder · Aldo Pacchiano · Michael Arbel · Michael Jordan -
2021 Poster: Structured Dropout Variational Inference for Bayesian Neural Networks »
Son Nguyen · Duong Nguyen · Khai Nguyen · Khoat Than · Hung Bui · Nhat Ho -
2021 Poster: On Robust Optimal Transport: Computational Complexity and Barycenter Computation »
Khang Le · Huy Nguyen · Quang M Nguyen · Tung Pham · Hung Bui · Nhat Ho -
2021 Poster: Learning Equilibria in Matching Markets from Bandit Feedback »
Meena Jagadeesan · Alexander Wei · Yixin Wang · Michael Jordan · Jacob Steinhardt -
2021 Poster: On Component Interactions in Two-Stage Recommender Systems »
Jiri Hron · Karl Krauth · Michael Jordan · Niki Kilbertus -
2020 : Contributed Talk 6: Do Offline Metrics Predict Online Performance in Recommender Systems? »
Karl Krauth · Sarah Dean · Wenshuo Guo · Benjamin Recht · Michael Jordan -
2020 Poster: Fixed-Support Wasserstein Barycenters: Computational Hardness and Fast Algorithm »
Tianyi Lin · Nhat Ho · Xi Chen · Marco Cuturi · Michael Jordan -
2020 Poster: Decision-Making with Auto-Encoding Variational Bayes »
Romain Lopez · Pierre Boyeau · Nir Yosef · Michael Jordan · Jeffrey Regier -
2020 Poster: Learning with Differentiable Pertubed Optimizers »
Quentin Berthet · Mathieu Blondel · Olivier Teboul · Marco Cuturi · Jean-Philippe Vert · Francis Bach -
2020 Poster: Transferable Calibration with Lower Bias and Variance in Domain Adaptation »
Ximei Wang · Mingsheng Long · Jianmin Wang · Michael Jordan -
2020 Poster: Entropic Optimal Transport between Unbalanced Gaussian Measures has a Closed Form »
Hicham Janati · Boris Muzellec · Gabriel Peyré · Marco Cuturi -
2020 Poster: Linear Time Sinkhorn Divergences using Positive Features »
Meyer Scetbon · Marco Cuturi -
2020 Oral: Entropic Optimal Transport between Unbalanced Gaussian Measures has a Closed Form »
Hicham Janati · Boris Muzellec · Gabriel Peyré · Marco Cuturi -
2020 Session: Orals & Spotlights Track 21: Optimization »
Peter Richtarik · Marco Cuturi -
2020 Poster: Robust Optimization for Fairness with Noisy Protected Groups »
Serena Wang · Wenshuo Guo · Harikrishna Narasimhan · Andrew Cotter · Maya Gupta · Michael Jordan -
2020 Poster: On the Theory of Transfer Learning: The Importance of Task Diversity »
Nilesh Tripuraneni · Michael Jordan · Chi Jin -
2020 Poster: On Function Approximation in Reinforcement Learning: Optimism in the Face of Large State Spaces »
Zhuoran Yang · Chi Jin · Zhaoran Wang · Mengdi Wang · Michael Jordan -
2019 : Poster and Coffee Break 2 »
Karol Hausman · Kefan Dong · Ken Goldberg · Lihong Li · Lin Yang · Lingxiao Wang · Lior Shani · Liwei Wang · Loren Amdahl-Culleton · Lucas Cassano · Marc Dymetman · Marc Bellemare · Marcin Tomczak · Margarita Castro · Marius Kloft · Marius-Constantin Dinu · Markus Holzleitner · Martha White · Mengdi Wang · Michael Jordan · Mihailo Jovanovic · Ming Yu · Minshuo Chen · Moonkyung Ryu · Muhammad Zaheer · Naman Agarwal · Nan Jiang · Niao He · Nikolaus Yasui · Nikos Karampatziakis · Nino Vieillard · Ofir Nachum · Olivier Pietquin · Ozan Sener · Pan Xu · Parameswaran Kamalaruban · Paul Mineiro · Paul Rolland · Philip Amortila · Pierre-Luc Bacon · Prakash Panangaden · Qi Cai · Qiang Liu · Quanquan Gu · Raihan Seraj · Richard Sutton · Rick Valenzano · Robert Dadashi · Rodrigo Toro Icarte · Roshan Shariff · Roy Fox · Ruosong Wang · Saeed Ghadimi · Samuel Sokota · Sean Sinclair · Sepp Hochreiter · Sergey Levine · Sergio Valcarcel Macua · Sham Kakade · Shangtong Zhang · Sheila McIlraith · Shie Mannor · Shimon Whiteson · Shuai Li · Shuang Qiu · Wai Lok Li · Siddhartha Banerjee · Sitao Luan · Tamer Basar · Thinh Doan · Tianhe Yu · Tianyi Liu · Tom Zahavy · Toryn Klassen · Tuo Zhao · Vicenç Gómez · Vincent Liu · Volkan Cevher · Wesley Suttle · Xiao-Wen Chang · Xiaohan Wei · Xiaotong Liu · Xingguo Li · Xinyi Chen · Xingyou Song · Yao Liu · YiDing Jiang · Yihao Feng · Yilun Du · Yinlam Chow · Yinyu Ye · Yishay Mansour · · Yonathan Efroni · Yongxin Chen · Yuanhao Wang · Bo Dai · Chen-Yu Wei · Harsh Shrivastava · Hongyang Zhang · Qinqing Zheng · SIDDHARTHA SATPATHI · Xueqing Liu · Andreu Vall -
2019 Workshop: Optimal Transport for Machine Learning »
Marco Cuturi · Gabriel Peyré · Rémi Flamary · Alexandra Suvorikova -
2019 Poster: Subspace Detours: Building Transport Plans that are Optimal on Subspace Projections »
Boris Muzellec · Marco Cuturi -
2019 Poster: Differentiable Ranking and Sorting using Optimal Transport »
Marco Cuturi · Olivier Teboul · Jean-Philippe Vert -
2019 Spotlight: Differentiable Ranking and Sorting using Optimal Transport »
Marco Cuturi · Olivier Teboul · Jean-Philippe Vert -
2019 Poster: Transferable Normalization: Towards Improving Transferability of Deep Neural Networks »
Ximei Wang · Ying Jin · Mingsheng Long · Jianmin Wang · Michael Jordan -
2019 Poster: Acceleration via Symplectic Discretization of High-Resolution Differential Equations »
Bin Shi · Simon Du · Weijie Su · Michael Jordan -
2019 Poster: Tree-Sliced Variants of Wasserstein Distances »
Tam Le · Makoto Yamada · Kenji Fukumizu · Marco Cuturi -
2018 Poster: Gen-Oja: Simple & Efficient Algorithm for Streaming Generalized Eigenvector Computation »
Kush Bhatia · Aldo Pacchiano · Nicolas Flammarion · Peter Bartlett · Michael Jordan -
2018 Poster: Large Scale computation of Means and Clusters for Persistence Diagrams using Optimal Transport »
Theo Lacombe · Marco Cuturi · Steve OUDOT -
2018 Poster: Theoretical guarantees for EM under misspecified Gaussian mixture models »
Raaz Dwivedi · nhật Hồ · Koulik Khamaru · Martin Wainwright · Michael Jordan -
2018 Poster: Stochastic Cubic Regularization for Fast Nonconvex Optimization »
Nilesh Tripuraneni · Mitchell Stern · Chi Jin · Jeffrey Regier · Michael Jordan -
2018 Poster: On the Local Minima of the Empirical Risk »
Chi Jin · Lydia T. Liu · Rong Ge · Michael Jordan -
2018 Spotlight: On the Local Minima of the Empirical Risk »
Chi Jin · Lydia T. Liu · Rong Ge · Michael Jordan -
2018 Oral: Stochastic Cubic Regularization for Fast Nonconvex Optimization »
Nilesh Tripuraneni · Mitchell Stern · Chi Jin · Jeffrey Regier · Michael Jordan -
2018 Poster: Is Q-Learning Provably Efficient? »
Chi Jin · Zeyuan Allen-Zhu · Sebastien Bubeck · Michael Jordan -
2018 Poster: Information Constraints on Auto-Encoding Variational Bayes »
Romain Lopez · Jeffrey Regier · Michael Jordan · Nir Yosef -
2018 Poster: Conditional Adversarial Domain Adaptation »
Mingsheng Long · ZHANGJIE CAO · Jianmin Wang · Michael Jordan -
2018 Poster: Generalized Zero-Shot Learning with Deep Calibration Network »
Shichen Liu · Mingsheng Long · Jianmin Wang · Michael Jordan -
2018 Poster: Generalizing Point Embeddings using the Wasserstein Space of Elliptical Distributions »
Boris Muzellec · Marco Cuturi -
2017 Workshop: Optimal Transport and Machine Learning »
Olivier Bousquet · Marco Cuturi · Gabriel Peyré · Fei Sha · Justin Solomon -
2017 Poster: Fast Black-box Variational Inference through Stochastic Trust-Region Optimization »
Jeffrey Regier · Michael Jordan · Jon McAuliffe -
2017 Poster: Online control of the false discovery rate with decaying memory »
Aaditya Ramdas · Fanny Yang · Martin Wainwright · Michael Jordan -
2017 Spotlight: Fast Black-box Variational Inference through Stochastic Trust-Region Optimization »
Jeffrey Regier · Michael Jordan · Jon McAuliffe -
2017 Oral: Online control of the false discovery rate with decaying memory »
Aaditya Ramdas · Fanny Yang · Martin Wainwright · Michael Jordan -
2017 Poster: Gradient Descent Can Take Exponential Time to Escape Saddle Points »
Simon Du · Chi Jin · Jason D Lee · Michael Jordan · Aarti Singh · Barnabas Poczos -
2017 Spotlight: Gradient Descent Can Take Exponential Time to Escape Saddle Points »
Simon Du · Chi Jin · Jason D Lee · Michael Jordan · Aarti Singh · Barnabas Poczos -
2017 Poster: Non-convex Finite-Sum Optimization Via SCSG Methods »
Lihua Lei · Cheng Ju · Jianbo Chen · Michael Jordan -
2017 Poster: Kernel Feature Selection via Conditional Covariance Minimization »
Jianbo Chen · Mitchell Stern · Martin J Wainwright · Michael Jordan -
2017 Tutorial: A Primer on Optimal Transport »
Marco Cuturi · Justin Solomon -
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: Time Series Workshop »
Oren Anava · Marco Cuturi · Azadeh Khaleghi · Vitaly Kuznetsov · Sasha Rakhlin -
2016 Poster: Cyclades: Conflict-free Asynchronous Machine Learning »
Xinghao Pan · Maximilian Lam · Stephen Tu · Dimitris Papailiopoulos · Ce Zhang · Michael Jordan · Kannan Ramchandran · Christopher Ré · Benjamin Recht -
2016 Poster: Wasserstein Training of Restricted Boltzmann Machines »
Grégoire Montavon · Klaus-Robert Müller · Marco Cuturi -
2016 Poster: Unsupervised Domain Adaptation with Residual Transfer Networks »
Mingsheng Long · Han Zhu · Jianmin Wang · Michael Jordan -
2016 Poster: Local Maxima in the Likelihood of Gaussian Mixture Models: Structural Results and Algorithmic Consequences »
Chi Jin · Yuchen Zhang · Sivaraman Balakrishnan · Martin J Wainwright · Michael Jordan -
2016 Poster: Stochastic Optimization for Large-scale Optimal Transport »
Aude Genevay · Marco Cuturi · Gabriel Peyré · Francis Bach -
2015 Poster: Variational Consensus Monte Carlo »
Maxim Rabinovich · Elaine Angelino · Michael Jordan -
2015 Poster: On the Accuracy of Self-Normalized Log-Linear Models »
Jacob Andreas · Maxim Rabinovich · Michael Jordan · Dan Klein -
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 Poster: Principal Geodesic Analysis for Probability Measures under the Optimal Transport Metric »
Vivien Seguy · Marco Cuturi -
2014 Workshop: Advances in Variational Inference »
David Blei · Shakir Mohamed · Michael Jordan · Charles Blundell · Tamara Broderick · Matthew D. Hoffman -
2014 Workshop: Optimal Transport and Machine Learning »
Marco Cuturi · Gabriel Peyré · Justin Solomon · Alexander Barvinok · Piotr Indyk · Robert McCann · Adam Oberman -
2014 Poster: Communication-Efficient Distributed Dual Coordinate Ascent »
Martin Jaggi · Virginia Smith · Martin Takac · Jonathan Terhorst · Sanjay Krishnan · Thomas Hofmann · Michael Jordan -
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 Spotlight: Spectral Methods meet EM: A Provably Optimal Algorithm for Crowdsourcing »
Yuchen Zhang · Xi Chen · Denny Zhou · Michael Jordan -
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 Poster: Sinkhorn Distances: Lightspeed Computation of Optimal Transport »
Marco Cuturi -
2013 Spotlight: Sinkhorn Distances: Lightspeed Computation of Optimal Transport »
Marco Cuturi -
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: 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 Poster: Optimistic Concurrency Control for Distributed Unsupervised Learning »
Xinghao Pan · Joseph Gonzalez · Stefanie Jegelka · Tamara Broderick · Michael Jordan -
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: 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: Privacy Aware Learning »
John Duchi · Michael Jordan · Martin J Wainwright -
2012 Poster: Ancestor Sampling for Particle Gibbs »
Fredrik Lindsten · Michael Jordan · Thomas Schön -
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: Small-Variance Asymptotics for Exponential Family Dirichlet Process Mixture Models »
Ke Jiang · Brian Kulis · Michael Jordan -
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 -
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 Poster: Variational Inference over Combinatorial Spaces »
Alexandre Bouchard-Côté · Michael Jordan -
2010 Poster: Unsupervised Kernel Dimension Reduction »
Meihong Wang · Fei Sha · Michael Jordan -
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: Nonparametric Bayes »
Dilan Gorur · Francois Caron · Yee Whye Teh · David B Dunson · Zoubin Ghahramani · Michael Jordan -
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: Asymptotically Optimal Regularization in Smooth Parametric Models »
Percy Liang · Francis Bach · Guillaume Bouchard · Michael Jordan -
2009 Poster: Nonparametric Latent Feature Models for Link Prediction »
Kurt T Miller · Tom Griffiths · Michael Jordan -
2009 Poster: White Functionals for Anomaly Detection in Dynamical Systems »
Marco Cuturi · Jean-Philippe Vert · Alexandre d'Aspremont -
2009 Spotlight: Nonparametric Latent Feature Models for Link Prediction »
Kurt T Miller · Tom Griffiths · Michael Jordan -
2008 Oral: Shared Segmentation of Natural Scenes Using Dependent Pitman-Yor Processes »
Erik Sudderth · Michael Jordan -
2008 Poster: Nonparametric Bayesian Learning of Switching Linear Dynamical Systems »
Emily Fox · Erik Sudderth · Michael Jordan · Alan S Willsky -
2008 Poster: High-dimensional union support recovery in multivariate regression »
Guillaume R Obozinski · Martin J Wainwright · Michael Jordan -
2008 Poster: Shared Segmentation of Natural Scenes Using Dependent Pitman-Yor Processes »
Erik Sudderth · Michael Jordan -
2008 Spotlight: High-dimensional union support recovery in multivariate regression »
Guillaume R Obozinski · Martin J Wainwright · Michael Jordan -
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: DiscLDA: Discriminative Learning for Dimensionality Reduction and Classification »
Simon Lacoste-Julien · Fei Sha · Michael Jordan -
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 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: Agreement-Based Learning »
Percy Liang · Dan Klein · Michael Jordan -
2007 Spotlight: Agreement-Based Learning »
Percy Liang · Dan Klein · Michael Jordan -
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: Kernels on Structured Objects Through Nested Histograms »
Marco Cuturi · Kenji Fukumizu -
2006 Poster: Distributed PCA and Network Anomaly Detection »
Ling Huang · XuanLong Nguyen · Minos Garofalakis · Michael Jordan · Anthony D Joseph · Nina Taft