Related Events (a corresponding poster, oral, or spotlight)

• 2007 Spotlight: Estimating divergence functionals and the likelihood ratio by penalized convex risk minimization »
  Tue. Dec 4th 04:10 -- 04:25 AM Room

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
• 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 : 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
• 2022 : Mechanisms that Incentivize Data Sharing in Federated Learning »
  Sai Praneeth Karimireddy · Wenshuo Guo · Michael Jordan
• 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: 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: 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: 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
• 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: Provable Benefits of Actor-Critic Methods for Offline Reinforcement Learning »
  Andrea Zanette · Martin J Wainwright · Emma Brunskill
• 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: 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: Projection Robust Wasserstein Distance and Riemannian Optimization »
  Tianyi Lin · Chenyou Fan · Nhat Ho · Marco Cuturi · Michael Jordan
• 2020 Poster: Fixed-Support Wasserstein Barycenters: Computational Hardness and Fast Algorithm »
  Tianyi Lin · Nhat Ho · Xi Chen · Marco Cuturi · Michael Jordan
• 2020 Spotlight: Projection Robust Wasserstein Distance and Riemannian Optimization »
  Tianyi Lin · Chenyou Fan · Nhat Ho · 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: Transferable Calibration with Lower Bias and Variance in Domain Adaptation »
  Ximei Wang · Mingsheng Long · Jianmin Wang · Michael Jordan
• 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 Poster: Transferable Normalization: Towards Improving Transferability of Deep Neural Networks »
  Ximei Wang · Ying Jin · Mingsheng Long · Jianmin Wang · Michael Jordan
• 2019 Poster: Scalable inference of topic evolution via models for latent geometric structures »
  Mikhail Yurochkin · Zhiwei Fan · Aritra Guha · Paraschos Koutris · XuanLong Nguyen
• 2019 Poster: Acceleration via Symplectic Discretization of High-Resolution Differential Equations »
  Bin Shi · Simon Du · Weijie Su · Michael Jordan
• 2018 Poster: Gen-Oja: Simple & Efficient Algorithm for Streaming Generalized Eigenvector Computation »
  Kush Bhatia · Aldo Pacchiano · Nicolas Flammarion · Peter Bartlett · Michael Jordan
• 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
• 2017 Poster: Fast Black-box Variational Inference through Stochastic Trust-Region Optimization »
  Jeffrey Regier · Michael Jordan · Jon McAuliffe
• 2017 Poster: Conic Scan-and-Cover algorithms for nonparametric topic modeling »
  Mikhail Yurochkin · Aritra Guha · XuanLong Nguyen
• 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: Multi-way Interacting Regression via Factorization Machines »
  Mikhail Yurochkin · XuanLong Nguyen · nikolaos Vasiloglou
• 2017 Poster: Kernel Feature Selection via Conditional Covariance Minimization »
  Jianbo Chen · Mitchell Stern · Martin J Wainwright · Michael Jordan
• 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 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: 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: Geometric Dirichlet Means Algorithm for topic inference »
  Mikhail Yurochkin · XuanLong Nguyen
• 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
• 2014 Workshop: Advances in Variational Inference »
  David Blei · Shakir Mohamed · Michael Jordan · Charles Blundell · Tamara Broderick · Matthew D. Hoffman
• 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: Parallel Feature Selection Inspired by Group Testing »
  Yingbo Zhou · Utkarsh Porwal · Ce Zhang · Hung Q Ngo · XuanLong Nguyen · Christopher Ré · Venu Govindaraju
• 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 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: Bayesian inference as iterated random functions with applications to sequential inference in graphical models »
  Arash Amini · XuanLong Nguyen
• 2013 Spotlight: Bayesian inference as iterated random functions with applications to sequential inference in graphical models »
  Arash Amini · XuanLong Nguyen
• 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: Communication-Efficient Algorithms for Statistical Optimization »
  Yuchen Zhang · John Duchi · Martin J Wainwright
• 2012 Poster: No voodoo here! Learning discrete graphical models via inverse covariance estimation »
  Po-Ling Loh · Martin J Wainwright
• 2012 Poster: Ancestor Sampling for Particle Gibbs »
  Fredrik Lindsten · Michael Jordan · Thomas Schön
• 2012 Oral: No voodoo here! Learning discrete graphical models via inverse covariance estimation »
  Po-Ling Loh · Martin J Wainwright
• 2012 Oral: Privacy Aware Learning »
  John Duchi · Michael Jordan · Martin J Wainwright
• 2012 Poster: Stochastic optimization and sparse statistical recovery: Optimal algorithms for high dimensions »
  Alekh Agarwal · Sahand N Negahban · 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
• 2011 Poster: A More Powerful Two-Sample Test in High Dimensions using Random Projection »
  Miles Lopes · Laurent Jacob · Martin J Wainwright
• 2011 Poster: High-dimensional regression with noisy and missing data: Provable guarantees with non-convexity »
  Po-Ling Loh · Martin J Wainwright
• 2011 Oral: High-dimensional regression with noisy and missing data: Provable guarantees with non-convexity »
  Po-Ling Loh · Martin J Wainwright
• 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 Spotlight: Distributed Dual Averaging In Networks »
  John Duchi · Alekh Agarwal · Martin J Wainwright
• 2010 Poster: Tree-Structured Stick Breaking for Hierarchical Data »
  Ryan Adams · Zoubin Ghahramani · Michael Jordan
• 2010 Poster: Distributed Dual Averaging In Networks »
  John Duchi · Alekh Agarwal · Martin J Wainwright
• 2010 Spotlight: Variational Inference over Combinatorial Spaces »
  Alexandre Bouchard-Côté · Michael Jordan
• 2010 Oral: Fast global convergence rates of gradient methods for high-dimensional statistical recovery »
  Alekh Agarwal · Sahand N Negahban · Martin J Wainwright
• 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: Fast global convergence rates of gradient methods for high-dimensional statistical recovery »
  Alekh Agarwal · Sahand N Negahban · Martin J Wainwright
• 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 Poster: Information-theoretic lower bounds on the oracle complexity of convex optimization »
  Alekh Agarwal · Peter Bartlett · Pradeep Ravikumar · Martin J Wainwright
• 2009 Poster: Lower bounds on minimax rates for nonparametric regression with additive sparsity and smoothness »
  Garvesh Raskutti · Martin J Wainwright · Bin Yu
• 2009 Spotlight: Lower bounds on minimax rates for nonparametric regression with additive sparsity and smoothness »
  Garvesh Raskutti · Martin J Wainwright · Bin Yu
• 2009 Spotlight: Information-theoretic lower bounds on the oracle complexity of convex optimization »
  Alekh Agarwal · Peter Bartlett · Pradeep Ravikumar · Martin J Wainwright
• 2009 Oral: Sharing Features among Dynamical Systems with Beta Processes »
  Emily Fox · Erik Sudderth · Michael Jordan · Alan S Willsky
• 2009 Poster: A unified framework for high-dimensional analysis of $M$-estimators with decomposable regularizers »
  Sahand N Negahban · Pradeep Ravikumar · Martin J Wainwright · Bin Yu
• 2009 Oral: A unified framework for high-dimensional analysis of $M$-estimators with decomposable regularizers »
  Sahand N Negahban · Pradeep Ravikumar · Martin J Wainwright · Bin Yu
• 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 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: Phase transitions for high-dimensional joint support recovery »
  Sahand N Negahban · Martin J Wainwright
• 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: Phase transitions for high-dimensional joint support recovery »
  Sahand N Negahban · Martin J Wainwright
• 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 Poster: Model Selection in Gaussian Graphical Models: High-Dimensional Consistency of \ell_1-regularizedMLE »
  Pradeep Ravikumar · Garvesh Raskutti · Martin J Wainwright · Bin Yu
• 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 Poster: Resampling Methods for Protein Structure Prediction with Rosetta »
  Ben Blum · David Baker · Michael Jordan · Philip Bradley · Rhiju Das · David Kim
• 2007 Poster: Loop Series and Bethe Variational Bounds in Attractive Graphical Models »
  Erik Sudderth · Martin J Wainwright · Alan S Willsky
• 2006 Poster: Distributed PCA and Network Anomaly Detection »
  Ling Huang · XuanLong Nguyen · Minos Garofalakis · Michael Jordan · Anthony D Joseph · Nina Taft
• 2006 Poster: Inferring Graphical Model Structure using $\ell_1$-Regularized Pseudo-Likelihood »
  Martin J Wainwright · Pradeep Ravikumar · John Lafferty
• 2006 Spotlight: Inferring Graphical Model Structure using $\ell_1$-Regularized Pseudo-Likelihood »
  Martin J Wainwright · Pradeep Ravikumar · John Lafferty