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ORCIDMichael I. Jordan
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- affiliation: University of California, Berkeley, Department of Electrical Engineering and Computer Science
- affiliation: University of California, Berkeley, Department of Statistics
- affiliation: Massachusetts Institute of Technology, Center for Biological and Computational Learning
- award (2009): ACM - AAAI Allen Newell Award
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2020 – today
- 2021
[j97]Chi Jin, Praneeth Netrapalli, Rong Ge, Sham M. Kakade, Michael I. Jordan:
On Nonconvex Optimization for Machine Learning: Gradients, Stochasticity, and Saddle Points. J. ACM 68(2): 11:1-11:29 (2021)- [j96]Tijana Zrnic, Aaditya Ramdas, Michael I. Jordan:
Asynchronous Online Testing of Multiple Hypotheses. J. Mach. Learn. Res. 22: 33:1-33:39 (2021) - [j95]Wenlong Mou, Yi-An Ma, Martin J. Wainwright, Peter L. Bartlett, Michael I. Jordan:
High-Order Langevin Diffusion Yields an Accelerated MCMC Algorithm. J. Mach. Learn. Res. 22: 42:1-42:41 (2021) - [j94]Jelena Diakonikolas
, Michael I. Jordan:
Generalized Momentum-Based Methods: A Hamiltonian Perspective. SIAM J. Optim. 31(1): 915-944 (2021) - [c318]Romain Lopez, Inderjit S. Dhillon, Michael I. Jordan:
Learning from eXtreme Bandit Feedback. AAAI 2021: 8732-8740 - [c317]Aldo Pacchiano, Heinrich Jiang, Michael I. Jordan:
Robustness Guarantees for Mode Estimation with an Application to Bandits. AAAI 2021: 9277-9284 - [c316]Tianyi Lin, Zeyu Zheng, Elynn Y. Chen, Marco Cuturi, Michael I. Jordan:
On Projection Robust Optimal Transport: Sample Complexity and Model Misspecification. AISTATS 2021: 262-270 - [c315]Jelena Diakonikolas, Constantinos Daskalakis, Michael I. Jordan:
Efficient Methods for Structured Nonconvex-Nonconcave Min-Max Optimization. AISTATS 2021: 2746-2754 - [i213]Stephen Bates, Anastasios Angelopoulos, Lihua Lei, Jitendra Malik, Michael I. Jordan:
Distribution-Free, Risk-Controlling Prediction Sets. CoRR abs/2101.02703 (2021) - [i212]Anastasios N. Angelopoulos, Stephen Bates, Tijana Zrnic, Michael I. Jordan:
Private Prediction Sets. CoRR abs/2102.06202 (2021) - [i211]Xiaowu Dai, Michael I. Jordan:
Multi-Stage Decentralized Matching Markets: Uncertain Preferences and Strategic Behaviors. CoRR abs/2102.06988 (2021) - [i210]Ani Adhikari, John DeNero, Michael I. Jordan:
Interleaving Computational and Inferential Thinking: Data Science for Undergraduates at Berkeley. CoRR abs/2102.09391 (2021) - [i209]Esther Rolf, Theodora Worledge, Benjamin Recht, Michael I. Jordan:
Representation Matters: Assessing the Importance of Subgroup Allocations in Training Data. CoRR abs/2103.03399 (2021) - [i208]Wenshuo Guo, Michael I. Jordan, Tianyi Lin:
A Variational Inequality Approach to Bayesian Regression Games. CoRR abs/2103.13509 (2021) - [i207]Tyler Westenbroek, Max Simchowitz, Michael I. Jordan, S. Shankar Sastry:
On the Stability of Nonlinear Receding Horizon Control: A Geometric Perspective. CoRR abs/2103.15010 (2021) - [i206]Wenshuo Guo, Serena Wang, Peng Ding, Yixin Wang, Michael I. Jordan:
Multi-Source Causal Inference Using Control Variates. CoRR abs/2103.16689 (2021) - [i205]Yaodong Yu, Tianyi Lin, Eric Mazumdar, Michael I. Jordan:
Fast Distributionally Robust Learning with Variance Reduced Min-Max Optimization. CoRR abs/2104.13326 (2021) - [i204]Jeffrey Chan, Aldo Pacchiano, Nilesh Tripuraneni, Yun S. Song, Peter L. Bartlett, Michael I. Jordan:
Parallelizing Contextual Linear Bandits. CoRR abs/2105.10590 (2021) - [i203]Niladri S. Chatterji, Aldo Pacchiano, Peter L. Bartlett, Michael I. Jordan:
On the Theory of Reinforcement Learning with Once-per-Episode Feedback. CoRR abs/2105.14363 (2021) - [i202]Brijen Thananjeyan, Kirthevasan Kandasamy, Ion Stoica, Michael I. Jordan, Ken Goldberg, Joseph E. Gonzalez:
PAC Best Arm Identification Under a Deadline. CoRR abs/2106.03221 (2021) - 2020
- [j93]Puyudi Yang, Jianbo Chen, Cho-Jui Hsieh, Jane-Ling Wang, Michael I. Jordan:
Greedy Attack and Gumbel Attack: Generating Adversarial Examples for Discrete Data. J. Mach. Learn. Res. 21: 43:1-43:36 (2020) - [j92]Lihua Lei, Michael I. Jordan:
On the Adaptivity of Stochastic Gradient-Based Optimization. SIAM J. Optim. 30(2): 1473-1500 (2020) - [c314]Jianbo Chen, Michael I. Jordan:
LS-Tree: Model Interpretation When the Data Are Linguistic. AAAI 2020: 3454-3461 - [c313]Romain Lopez, Chenchen Li, Xiang Yan, Junwu Xiong, Michael I. Jordan, Yuan Qi, Le Song:
Cost-Effective Incentive Allocation via Structured Counterfactual Inference. AAAI 2020: 4997-5004 - [c312]Puyudi Yang, Jianbo Chen, Cho-Jui Hsieh, Jane-Ling Wang, Michael I. Jordan:
ML-LOO: Detecting Adversarial Examples with Feature Attribution. AAAI 2020: 6639-6647 - [c311]Lydia T. Liu, Horia Mania, Michael I. Jordan:
Competing Bandits in Matching Markets. AISTATS 2020: 1618-1628 - [c310]Niladri S. Chatterji, Jelena Diakonikolas, Michael I. Jordan, Peter L. Bartlett:
Langevin Monte Carlo without smoothness. AISTATS 2020: 1716-1726 - [c309]Esther Rolf, Michael I. Jordan, Benjamin Recht:
Post-Estimation Smoothing: A Simple Baseline for Learning with Side Information. AISTATS 2020: 1759-1769 - [c308]Raaz Dwivedi, Nhat Ho, Koulik Khamaru, Martin J. Wainwright, Michael I. Jordan, Bin Yu:
Sharp Analysis of Expectation-Maximization for Weakly Identifiable Models. AISTATS 2020: 1866-1876 - [c307]Wenshuo Guo, Nhat Ho, Michael I. Jordan:
Fast Algorithms for Computational Optimal Transport and Wasserstein Barycenter. AISTATS 2020: 2088-2097 - [c306]Jonathan N. Lee, Aldo Pacchiano, Michael I. Jordan:
Convergence Rates of Smooth Message Passing with Rounding in Entropy-Regularized MAP Inference. AISTATS 2020: 3003-3014 - [c305]Tijana Zrnic, Daniel Jiang, Aaditya Ramdas, Michael I. Jordan:
The Power of Batching in Multiple Hypothesis Testing. AISTATS 2020: 3806-3815 - [c304]Tianyi Lin, Chengyou Fan, Mengdi Wang, Michael I. Jordan:
Improved Sample Complexity for Stochastic Compositional Variance Reduced Gradient. ACC 2020: 126-131 - [c303]Eric Mazumdar, Lillian J. Ratliff, Michael I. Jordan, S. Shankar Sastry:
Policy-Gradient Algorithms Have No Guarantees of Convergence in Linear Quadratic Games. AAMAS 2020: 860-868 - [c302]Eric Mazumdar, Tyler Westenbroek, Michael I. Jordan, S. Shankar Sastry:
High Confidence Sets for Trajectories of Stochastic Time-Varying Nonlinear Systems. CDC 2020: 4275-4280 - [c301]Chi Jin, Zhuoran Yang, Zhaoran Wang, Michael I. Jordan:
Provably efficient reinforcement learning with linear function approximation. COLT 2020: 2137-2143 - [c300]Tianyi Lin, Chi Jin, Michael I. Jordan:
Near-Optimal Algorithms for Minimax Optimization. COLT 2020: 2738-2779 - [c299]Wenlong Mou, Chris Junchi Li, Martin J. Wainwright, Peter L. Bartlett, Michael I. Jordan:
On Linear Stochastic Approximation: Fine-grained Polyak-Ruppert and Non-Asymptotic Concentration. COLT 2020: 2947-2997 - [c298]Adelson Chua, Michael I. Jordan, Rikky Muller:
Unsupervised Online Learning for Long-Term High Sensitivity Seizure Detection. EMBC 2020: 528-531 - [c297]Melih Elibol, Lihua Lei, Michael I. Jordan:
Variance Reduction With Sparse Gradients. ICLR 2020 - [c296]Xiang Cheng, Dong Yin, Peter L. Bartlett, Michael I. Jordan:
Stochastic Gradient and Langevin Processes. ICML 2020: 1810-1819 - [c295]Chi Jin, Praneeth Netrapalli, Michael I. Jordan:
What is Local Optimality in Nonconvex-Nonconcave Minimax Optimization? ICML 2020: 4880-4889 - [c294]Jonathan N. Lee, Aldo Pacchiano, Peter L. Bartlett, Michael I. Jordan:
Accelerated Message Passing for Entropy-Regularized MAP Inference. ICML 2020: 5736-5746 - [c293]Tianyi Lin, Chi Jin, Michael I. Jordan:
On Gradient Descent Ascent for Nonconvex-Concave Minimax Problems. ICML 2020: 6083-6093 - [c292]Tianyi Lin, Zhengyuan Zhou, Panayotis Mertikopoulos, Michael I. Jordan:
Finite-Time Last-Iterate Convergence for Multi-Agent Learning in Games. ICML 2020: 6161-6171 - [c291]Eric Mazumdar, Aldo Pacchiano, Yi-An Ma, Michael I. Jordan, Peter L. Bartlett:
On Approximate Thompson Sampling with Langevin Algorithms. ICML 2020: 6797-6807 - [c290]Michael Muehlebach, Michael I. Jordan:
Continuous-time Lower Bounds for Gradient-based Algorithms. ICML 2020: 7088-7096 - [c289]Aldo Pacchiano, Jack Parker-Holder, Yunhao Tang, Krzysztof Choromanski, Anna Choromanska, Michael I. Jordan:
Learning to Score Behaviors for Guided Policy Optimization. ICML 2020: 7445-7454 - [c288]Tianyi Lin, Chenyou Fan, Nhat Ho, Marco Cuturi, Michael I. Jordan:
Projection Robust Wasserstein Distance and Riemannian Optimization. NeurIPS 2020 - [c287]Tianyi Lin, Nhat Ho, Xi Chen, Marco Cuturi, Michael I. Jordan:
Fixed-Support Wasserstein Barycenters: Computational Hardness and Fast Algorithm. NeurIPS 2020 - [c286]Romain Lopez, Pierre Boyeau, Nir Yosef, Michael I. Jordan, Jeffrey Regier:
Decision-Making with Auto-Encoding Variational Bayes. NeurIPS 2020 - [c285]Nilesh Tripuraneni, Michael I. Jordan, Chi Jin:
On the Theory of Transfer Learning: The Importance of Task Diversity. NeurIPS 2020 - [c284]Serena Wang, Wenshuo Guo, Harikrishna Narasimhan, Andrew Cotter, Maya R. Gupta, Michael I. Jordan:
Robust Optimization for Fairness with Noisy Protected Groups. NeurIPS 2020 - [c283]Ximei Wang, Mingsheng Long, Jianmin Wang, Michael I. Jordan:
Transferable Calibration with Lower Bias and Variance in Domain Adaptation. NeurIPS 2020 - [c282]Zhuoran Yang, Chi Jin, Zhaoran Wang, Mengdi Wang, Michael I. Jordan:
Provably Efficient Reinforcement Learning with Kernel and Neural Function Approximations. NeurIPS 2020 - [c281]Jianbo Chen, Michael I. Jordan, Martin J. Wainwright:
HopSkipJumpAttack: A Query-Efficient Decision-Based Attack. IEEE Symposium on Security and Privacy 2020: 1277-1294 - [i201]Melih Elibol, Lihua Lei, Michael I. Jordan:
Variance Reduction with Sparse Gradients. CoRR abs/2001.09623 (2020) - [i200]Tianyi Lin, Chi Jin, Michael I. Jordan:
Near-Optimal Algorithms for Minimax Optimization. CoRR abs/2002.02417 (2020) - [i199]Michael Muehlebach, Michael I. Jordan:
Continuous-time Lower Bounds for Gradient-based Algorithms. CoRR abs/2002.03546 (2020) - [i198]Tianyi Lin, Nhat Ho, Xi Chen, Marco Cuturi, Michael I. Jordan:
Revisiting Fixed Support Wasserstein Barycenter: Computational Hardness and Efficient Algorithms. CoRR abs/2002.04783 (2020) - [i197]Samuel Horváth, Lihua Lei, Peter Richtárik, Michael I. Jordan:
Adaptivity of Stochastic Gradient Methods for Nonconvex Optimization. CoRR abs/2002.05359 (2020) - [i196]Romain Lopez, Pierre Boyeau, Nir Yosef, Michael I. Jordan, Jeffrey Regier:
Decision-Making with Auto-Encoding Variational Bayes. CoRR abs/2002.07217 (2020) - [i195]Serena Wang, Wenshuo Guo, Harikrishna Narasimhan, Andrew Cotter, Maya R. Gupta, Michael I. Jordan:
Robust Optimization for Fairness with Noisy Protected Groups. CoRR abs/2002.09343 (2020) - [i194]Tianyi Lin, Zhengyuan Zhou, Panayotis Mertikopoulos, Michael I. Jordan:
Finite-Time Last-Iterate Convergence for Multi-Agent Learning in Games. CoRR abs/2002.09806 (2020) - [i193]Eric Mazumdar, Aldo Pacchiano, Yi-An Ma, Peter L. Bartlett, Michael I. Jordan:
On Thompson Sampling with Langevin Algorithms. CoRR abs/2002.10002 (2020) - [i192]Nilesh Tripuraneni, Chi Jin, Michael I. Jordan:
Provable Meta-Learning of Linear Representations. CoRR abs/2002.11684 (2020) - [i191]Michael Muehlebach, Michael I. Jordan:
Optimization with Momentum: Dynamical, Control-Theoretic, and Symplectic Perspectives. CoRR abs/2002.12493 (2020) - [i190]Aldo Pacchiano, Heinrich Jiang, Michael I. Jordan:
Robustness Guarantees for Mode Estimation with an Application to Bandits. CoRR abs/2003.02932 (2020) - [i189]Esther Rolf, Michael I. Jordan, Benjamin Recht:
Post-Estimation Smoothing: A Simple Baseline for Learning with Side Information. CoRR abs/2003.05955 (2020) - [i188]Koulik Khamaru, Ashwin Pananjady, Feng Ruan, Martin J. Wainwright, Michael I. Jordan:
Is Temporal Difference Learning Optimal? An Instance-Dependent Analysis. CoRR abs/2003.07337 (2020) - [i187]Wenlong Mou, Chris Junchi Li, Martin J. Wainwright, Peter L. Bartlett, Michael I. Jordan:
On Linear Stochastic Approximation: Fine-grained Polyak-Ruppert and Non-Asymptotic Concentration. CoRR abs/2004.04719 (2020) - [i186]Bin Shi, Weijie J. Su, Michael I. Jordan:
On Learning Rates and Schrödinger Operators. CoRR abs/2004.06977 (2020) - [i185]Kirthevasan Kandasamy, Joseph E. Gonzalez, Michael I. Jordan, Ion Stoica:
Mechanism Design with Bandit Feedback. CoRR abs/2004.08924 (2020) - [i184]Max Rabinovich, Michael I. Jordan, Martin J. Wainwright:
Lower bounds in multiple testing: A framework based on derandomized proxies. CoRR abs/2005.03725 (2020) - [i183]Nhat Ho, Koulik Khamaru, Raaz Dwivedi, Martin J. Wainwright, Michael I. Jordan, Bin Yu:
Instability, Computational Efficiency and Statistical Accuracy. CoRR abs/2005.11411 (2020) - [i182]Tianyi Lin, Chenyou Fan, Nhat Ho, Marco Cuturi, Michael I. Jordan:
Projection Robust Wasserstein Distance and Riemannian Optimization. CoRR abs/2006.07458 (2020) - [i181]Horia Mania, Michael I. Jordan, Benjamin Recht:
Active Learning for Nonlinear System Identification with Guarantees. CoRR abs/2006.10277 (2020) - [i180]Nilesh Tripuraneni, Michael I. Jordan, Chi Jin:
On the Theory of Transfer Learning: The Importance of Task Diversity. CoRR abs/2006.11650 (2020) - [i179]Tianyi Lin, Zeyu Zheng, Elynn Y. Chen, Marco Cuturi, Michael I. Jordan:
On Projection Robust Optimal Transport: Sample Complexity and Model Misspecification. CoRR abs/2006.12301 (2020) - [i178]Jonathan N. Lee, Aldo Pacchiano, Peter L. Bartlett, Michael I. Jordan:
Accelerated Message Passing for Entropy-Regularized MAP Inference. CoRR abs/2007.00699 (2020) - [i177]Daniel Ting, Michael I. Jordan:
Manifold Learning via Manifold Deflation. CoRR abs/2007.03315 (2020) - [i176]Wenshuo Guo, Mihaela Curmei, Serena Wang, Benjamin Recht, Michael I. Jordan:
Finding Equilibrium in Multi-Agent Games with Payoff Uncertainty. CoRR abs/2007.05647 (2020) - [i175]Yeshwanth Cherapanamjeri, Efe Aras, Nilesh Tripuraneni, Michael I. Jordan, Nicolas Flammarion, Peter L. Bartlett:
Optimal Robust Linear Regression in Nearly Linear Time. CoRR abs/2007.08137 (2020) - [i174]Ximei Wang, Mingsheng Long, Jianmin Wang, Michael I. Jordan:
Transferable Calibration with Lower Bias and Variance in Domain Adaptation. CoRR abs/2007.08259 (2020) - [i173]Yuchen Zhang, Mingsheng Long, Jianmin Wang, Michael I. Jordan:
On Localized Discrepancy for Domain Adaptation. CoRR abs/2008.06242 (2020) - [i172]Chris Junchi Li, Wenlong Mou, Martin J. Wainwright, Michael I. Jordan:
ROOT-SGD: Sharp Nonasymptotics and Asymptotic Efficiency in a Single Algorithm. CoRR abs/2008.12690 (2020) - [i171]Jiri Hron, Karl Krauth, Michael I. Jordan, Niki Kilbertus:
Exploration in two-stage recommender systems. CoRR abs/2009.08956 (2020) - [i170]Romain Lopez, Inderjit S. Dhillon, Michael I. Jordan:
Learning from eXtreme Bandit Feedback. CoRR abs/2009.12947 (2020) - [i169]Anastasios Angelopoulos, Stephen Bates, Jitendra Malik, Michael I. Jordan:
Uncertainty Sets for Image Classifiers using Conformal Prediction. CoRR abs/2009.14193 (2020) - [i168]Xiaowu Dai, Michael I. Jordan:
Learning Strategies in Decentralized Matching Markets under Uncertain Preferences. CoRR abs/2011.00159 (2020) - [i167]Brijen Thananjeyan, Kirthevasan Kandasamy, Ion Stoica, Michael I. Jordan, Ken Goldberg, Joseph E. Gonzalez:
Resource Allocation in Multi-armed Bandit Exploration: Overcoming Nonlinear Scaling with Adaptive Parallelism. CoRR abs/2011.00330 (2020) - [i166]Jelena Diakonikolas, Constantinos Daskalakis, Michael I. Jordan:
Efficient Methods for Structured Nonconvex-Nonconcave Min-Max Optimization. CoRR abs/2011.00364 (2020) - [i165]Zhuoran Yang, Chi Jin, Zhaoran Wang, Mengdi Wang, Michael I. Jordan:
Bridging Exploration and General Function Approximation in Reinforcement Learning: Provably Efficient Kernel and Neural Value Iterations. CoRR abs/2011.04622 (2020) - [i164]Karl Krauth, Sarah Dean, Alex Zhao, Wenshuo Guo, Mihaela Curmei, Benjamin Recht, Michael I. Jordan:
Do Offline Metrics Predict Online Performance in Recommender Systems? CoRR abs/2011.07931 (2020) - [i163]Yeshwanth Cherapanamjeri, Nilesh Tripuraneni, Peter L. Bartlett, Michael I. Jordan:
Optimal Mean Estimation without a Variance. CoRR abs/2011.12433 (2020) - [i162]Lydia T. Liu, Feng Ruan, Horia Mania, Michael I. Jordan:
Bandit Learning in Decentralized Matching Markets. CoRR abs/2012.07348 (2020) - [i161]Kirthevasan Kandasamy, Gur-Eyal Sela, Joseph E. Gonzalez, Michael I. Jordan, Ion Stoica:
Online Learning Demands in Max-min Fairness. CoRR abs/2012.08648 (2020) - [i160]Chris Junchi Li, Michael I. Jordan:
Stochastic Approximation for Online Tensorial Independent Component Analysis. CoRR abs/2012.14415 (2020)
2010 – 2019
- 2019
- [j91]Jason D. Lee, Ioannis Panageas, Georgios Piliouras, Max Simchowitz, Michael I. Jordan, Benjamin Recht:
First-order methods almost always avoid strict saddle points. Math. Program. 176(1-2): 311-337 (2019) - [j90]Mingsheng Long, Yue Cao, Zhangjie Cao, Jianmin Wang, Michael I. Jordan:
Transferable Representation Learning with Deep Adaptation Networks. IEEE Trans. Pattern Anal. Mach. Intell. 41(12): 3071-3085 (2019) - [j89]Ahmed El Alaoui, Aaditya Ramdas, Florent Krzakala, Lenka Zdeborová, Michael I. Jordan:
Decoding from Pooled Data: Sharp Information-Theoretic Bounds. SIAM J. Math. Data Sci. 1(1): 161-188 (2019) - [j88]Ahmed El Alaoui, Aaditya Ramdas, Florent Krzakala, Lenka Zdeborová, Michael I. Jordan:
Decoding From Pooled Data: Phase Transitions of Message Passing. IEEE Trans. Inf. Theory 65(1): 572-585 (2019) - [c280]Ryan Giordano, William T. Stephenson, Runjing Liu, Michael I. Jordan, Tamara Broderick:
A Swiss Army Infinitesimal Jackknife. AISTATS 2019: 1139-1147 - [c279]Nhat Ho, Viet Huynh, Dinh Q. Phung, Michael I. Jordan:
Probabilistic Multilevel Clustering via Composite Transportation Distance. AISTATS 2019: 3149-3157 - [c278]Kaichao You, Mingsheng Long, Zhangjie Cao, Jianmin Wang, Michael I. Jordan:
Universal Domain Adaptation. CVPR 2019: 2720-2729 - [c277]Jianbo Chen, Le Song, Martin J. Wainwright, Michael I. Jordan:
L-Shapley and C-Shapley: Efficient Model Interpretation for Structured Data. ICLR (Poster) 2019 - [c276]Tianyi Lin, Nhat Ho, Michael I. Jordan:
On Efficient Optimal Transport: An Analysis of Greedy and Accelerated Mirror Descent Algorithms. ICML 2019: 3982-3991 - [c275]Hong Liu, Mingsheng Long, Jianmin Wang, Michael I. Jordan:
Transferable Adversarial Training: A General Approach to Adapting Deep Classifiers. ICML 2019: 4013-4022 - [c274]Runjing Liu, Jeffrey Regier, Nilesh Tripuraneni, Michael I. Jordan, Jon D. McAuliffe:
Rao-Blackwellized Stochastic Gradients for Discrete Distributions. ICML 2019: 4023-4031 - [c273]Michael Muehlebach, Michael I. Jordan:
A Dynamical Systems Perspective on Nesterov Acceleration. ICML 2019: 4656-4662 - [c272]Kaichao You, Ximei Wang, Mingsheng Long, Michael I. Jordan:
Towards Accurate Model Selection in Deep Unsupervised Domain Adaptation. ICML 2019: 7124-7133 - [c271]Yuchen Zhang, Tianle Liu, Mingsheng Long, Michael I. Jordan:
Bridging Theory and Algorithm for Domain Adaptation. ICML 2019: 7404-7413 - [c270]Hongyang Zhang, Yaodong Yu, Jiantao Jiao, Eric P. Xing, Laurent El Ghaoui, Michael I. Jordan:
Theoretically Principled Trade-off between Robustness and Accuracy. ICML 2019: 7472-7482 - [c269]Ximei Wang, Ying Jin, Mingsheng Long, Jianmin Wang, Michael I. Jordan:
Transferable Normalization: Towards Improving Transferability of Deep Neural Networks. NeurIPS 2019: 1951-1961 - [c268]Bin Shi, Simon S. Du, Weijie J. Su, Michael I. Jordan:
Acceleration via Symplectic Discretization of High-Resolution Differential Equations. NeurIPS 2019: 5745-5753 - [i159]Eric V. Mazumdar, Michael I. Jordan, S. Shankar Sastry:
On Finding Local Nash Equilibria (and Only Local Nash Equilibria) in Zero-Sum Games. CoRR abs/1901.00838 (2019) - [i158]Tianyi Lin, Nhat Ho, Michael I. Jordan:
On Efficient Optimal Transport: An Analysis of Greedy and Accelerated Mirror Descent Algorithms. CoRR abs/1901.06482 (2019) - [i157]Hongyang Zhang, Yaodong Yu, Jiantao Jiao, Eric P. Xing, Laurent El Ghaoui, Michael I. Jordan:
Theoretically Principled Trade-off between Robustness and Accuracy. CoRR abs/1901.08573 (2019) - [i156]Raaz Dwivedi, Nhat Ho, Koulik Khamaru, Martin J. Wainwright, Michael I. Jordan, Bin Yu:
Challenges with EM in application to weakly identifiable mixture models. CoRR abs/1902.00194 (2019) - [i155]Chi Jin, Praneeth Netrapalli, Michael I. Jordan:
Minmax Optimization: Stable Limit Points of Gradient Descent Ascent are Locally Optimal. CoRR abs/1902.00618 (2019) - [i154]Xiang Cheng, Peter L. Bartlett, Michael I. Jordan:
Quantitative Central Limit Theorems for Discrete Stochastic Processes. CoRR abs/1902.00832 (2019) - [i153]Yi-An Ma, Niladri S. Chatterji, Xiang Cheng, Nicolas Flammarion, Peter L. Bartlett, Michael I. Jordan:
Is There an Analog of Nesterov Acceleration for MCMC? CoRR abs/1902.00996 (2019) - [i152]Romain Lopez, Chenchen Li, Xiang Yan, Junwu Xiong, Michael I. Jordan, Yuan Qi, Le Song:
Cost-Effective Incentive Allocation via Structured Counterfactual Inference. CoRR abs/1902.02495 (2019) - [i151]Bin Shi, Simon S. Du, Weijie J. Su, Michael I. Jordan:
Acceleration via Symplectic Discretization of High-Resolution Differential Equations. CoRR abs/1902.03694 (2019) - [i150]Chi Jin, Praneeth Netrapalli, Rong Ge, Sham M. Kakade, Michael I. Jordan:
A Short Note on Concentration Inequalities for Random Vectors with SubGaussian Norm. CoRR abs/1902.03736 (2019) - [i149]Jianbo Chen, Michael I. Jordan:
LS-Tree: Model Interpretation When the Data Are Linguistic. CoRR abs/1902.04187 (2019) - [i148]