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2020 – today
- 2024
[j3]Max Simchowitz
, Aleksandrs Slivkins:
Exploration and Incentives in Reinforcement Learning. Oper. Res. 72(3): 983-998 (2024)- [c46]Adam Block, Dylan J. Foster, Akshay Krishnamurthy, Max Simchowitz, Cyril Zhang:
Butterfly Effects of SGD Noise: Error Amplification in Behavior Cloning and Autoregression. ICLR 2024 - [c45]Lirui Wang, Kaiqing Zhang, Allan Zhou, Max Simchowitz, Russ Tedrake:
Robot Fleet Learning via Policy Merging. ICLR 2024 - [c44]Thomas Cohn, Seiji Shaw, Max Simchowitz, Russ Tedrake:
Constrained Bimanual Planning with Analytic Inverse Kinematics. ICRA 2024: 6935-6942 - [i52]Boyuan Chen, Diego Marti Monso, Yilun Du, Max Simchowitz, Russ Tedrake, Vincent Sitzmann:
Diffusion Forcing: Next-token Prediction Meets Full-Sequence Diffusion. CoRR abs/2407.01392 (2024) - [i51]Allen Z. Ren, Justin Lidard, Lars Ankile, Anthony Simeonov, Pulkit Agrawal, Anirudha Majumdar, Benjamin Burchfiel, Hongkai Dai, Max Simchowitz:
Diffusion Policy Policy Optimization. CoRR abs/2409.00588 (2024) - 2023
- [c43]Adam Block, Max Simchowitz, Alexander Rakhlin:
Oracle-Efficient Smoothed Online Learning for Piecewise Continuous Decision Making. COLT 2023: 1618-1665 - [c42]Max Simchowitz, Abhishek Gupta, Kaiqing Zhang:
Tackling Combinatorial Distribution Shift: A Matrix Completion Perspective. COLT 2023: 3356-3468 - [c41]Aviv Netanyahu, Abhishek Gupta, Max Simchowitz, Kaiqing Zhang, Pulkit Agrawal:
Learning to Extrapolate: A Transductive Approach. ICLR 2023 - [c40]Daniel Pfrommer, Max Simchowitz, Tyler Westenbroek, Nikolai Matni, Stephen Tu:
The Power of Learned Locally Linear Models for Nonlinear Policy Optimization. ICML 2023: 27737-27821 - [c39]Max Simchowitz, Anurag Ajay, Pulkit Agrawal, Akshay Krishnamurthy:
Statistical Learning under Heterogenous Distribution Shift. ICML 2023: 31800-31851 - [c38]Adam Block, Ali Jadbabaie, Daniel Pfrommer, Max Simchowitz, Russ Tedrake:
Provable Guarantees for Generative Behavior Cloning: Bridging Low-Level Stability and High-Level Behavior. NeurIPS 2023 - [c37]Adam Block, Max Simchowitz, Russ Tedrake:
Smoothed Online Learning for Prediction in Piecewise Affine Systems. NeurIPS 2023 - [c36]Chuning Zhu, Max Simchowitz, Siri Gadipudi, Abhishek Gupta:
RePo: Resilient Model-Based Reinforcement Learning by Regularizing Posterior Predictability. NeurIPS 2023 - [c35]Thomas Cohn, Mark Petersen, Max Simchowitz, Russ Tedrake:
Non-Euclidean Motion Planning with Graphs of Geodesically-Convex Sets. Robotics: Science and Systems 2023 - [i50]Adam Block, Max Simchowitz, Russ Tedrake:
Smoothed Online Learning for Prediction in Piecewise Affine Systems. CoRR abs/2301.11187 (2023) - [i49]Adam Block, Alexander Rakhlin, Max Simchowitz:
Oracle-Efficient Smoothed Online Learning for Piecewise Continuous Decision Making. CoRR abs/2302.05430 (2023) - [i48]Max Simchowitz, Anurag Ajay, Pulkit Agrawal, Akshay Krishnamurthy:
Statistical Learning under Heterogenous Distribution Shift. CoRR abs/2302.13934 (2023) - [i47]Aviv Netanyahu, Abhishek Gupta, Max Simchowitz, Kaiqing Zhang, Pulkit Agrawal:
Learning to Extrapolate: A Transductive Approach. CoRR abs/2304.14329 (2023) - [i46]Thomas Cohn, Mark Petersen, Max Simchowitz, Russ Tedrake:
Non-Euclidean Motion Planning with Graphs of Geodesically-Convex Sets. CoRR abs/2305.06341 (2023) - [i45]Daniel Pfrommer, Max Simchowitz, Tyler Westenbroek, Nikolai Matni, Stephen Tu:
The Power of Learned Locally Linear Models for Nonlinear Policy Optimization. CoRR abs/2305.09619 (2023) - [i44]Max Simchowitz, Abhishek Gupta, Kaiqing Zhang:
Tackling Combinatorial Distribution Shift: A Matrix Completion Perspective. CoRR abs/2307.06457 (2023) - [i43]Adam Block, Daniel Pfrommer, Max Simchowitz:
Imitating Complex Trajectories: Bridging Low-Level Stability and High-Level Behavior. CoRR abs/2307.14619 (2023) - [i42]Chuning Zhu, Max Simchowitz, Siri Gadipudi, Abhishek Gupta:
RePo: Resilient Model-Based Reinforcement Learning by Regularizing Posterior Predictability. CoRR abs/2309.00082 (2023) - [i41]Thomas Cohn, Seiji Shaw, Max Simchowitz, Russ Tedrake:
Constrained Bimanual Planning with Analytic Inverse Kinematics. CoRR abs/2309.08770 (2023) - [i40]Lirui Wang, Kaiqing Zhang, Allan Zhou, Max Simchowitz, Russ Tedrake:
Fleet Policy Learning via Weight Merging and An Application to Robotic Tool-Use. CoRR abs/2310.01362 (2023) - [i39]Adam Block, Dylan J. Foster, Akshay Krishnamurthy, Max Simchowitz, Cyril Zhang:
Butterfly Effects of SGD Noise: Error Amplification in Behavior Cloning and Autoregression. CoRR abs/2310.11428 (2023) - 2022
- [c34]Andrew J. Wagenmaker, Max Simchowitz, Kevin Jamieson:
Beyond No Regret: Instance-Dependent PAC Reinforcement Learning. COLT 2022: 358-418 - [c33]Hyung Ju Suh, Max Simchowitz, Kaiqing Zhang, Russ Tedrake:
Do Differentiable Simulators Give Better Policy Gradients? ICML 2022: 20668-20696 - [c32]Andrew J. Wagenmaker, Yifang Chen, Max Simchowitz, Simon S. Du, Kevin G. Jamieson:
First-Order Regret in Reinforcement Learning with Linear Function Approximation: A Robust Estimation Approach. ICML 2022: 22384-22429 - [c31]Andrew J. Wagenmaker, Yifang Chen, Max Simchowitz, Simon S. Du, Kevin G. Jamieson:
Reward-Free RL is No Harder Than Reward-Aware RL in Linear Markov Decision Processes. ICML 2022: 22430-22456 - [c30]Adam Block, Max Simchowitz:
Efficient and Near-Optimal Smoothed Online Learning for Generalized Linear Functions. NeurIPS 2022 - [c29]Jack Umenberger, Max Simchowitz, Juan C. Perdomo, Kaiqing Zhang, Russ Tedrake:
Globally Convergent Policy Search for Output Estimation. NeurIPS 2022 - [i38]Andrew Wagenmaker, Yifang Chen, Max Simchowitz, Simon S. Du, Kevin Jamieson:
Reward-Free RL is No Harder Than Reward-Aware RL in Linear Markov Decision Processes. CoRR abs/2201.11206 (2022) - [i37]H. J. Terry Suh, Max Simchowitz, Kaiqing Zhang, Russ Tedrake:
Do Differentiable Simulators Give Better Policy Gradients? CoRR abs/2202.00817 (2022) - [i36]Edgar Minasyan, Paula Gradu, Max Simchowitz, Elad Hazan:
Online Control of Unknown Time-Varying Dynamical Systems. CoRR abs/2202.07890 (2022) - [i35]Jack Umenberger, Max Simchowitz, Juan C. Perdomo, Kaiqing Zhang, Russ Tedrake:
Globally Convergent Policy Search over Dynamic Filters for Output Estimation. CoRR abs/2202.11659 (2022) - [i34]Adam Block, Max Simchowitz:
Efficient and Near-Optimal Smoothed Online Learning for Generalized Linear Functions. CoRR abs/2205.13056 (2022) - 2021
- [b1]Max Simchowitz:
Statistical Complexity and Regret in Linear Control. University of California, Berkeley, USA, 2021 - [j2]Esther Rolf, David Fridovich-Keil, Max Simchowitz, Benjamin Recht, Claire J. Tomlin:
A Successive-Elimination Approach to Adaptive Robotic Source Seeking. IEEE Trans. Robotics 37(1): 34-47 (2021) - [c28]Tyler Westenbroek, Max Simchowitz, Michael I. Jordan, S. Shankar Sastry:
On the Stability of Nonlinear Receding Horizon Control: A Geometric Perspective. CDC 2021: 742-749 - [c27]Thodoris Lykouris, Max Simchowitz, Alex Slivkins, Wen Sun:
Corruption-robust exploration in episodic reinforcement learning. COLT 2021: 3242-3245 - [c26]Juan C. Perdomo, Max Simchowitz, Alekh Agarwal, Peter L. Bartlett:
Towards a Dimension-Free Understanding of Adaptive Linear Control. COLT 2021: 3681-3770 - [c25]Andrew J. Wagenmaker, Max Simchowitz, Kevin G. Jamieson:
Task-Optimal Exploration in Linear Dynamical Systems. ICML 2021: 10641-10652 - [c24]Edgar Minasyan, Paula Gradu, Max Simchowitz, Elad Hazan:
Online Control of Unknown Time-Varying Dynamical Systems. NeurIPS 2021: 15934-15945 - [c23]Max Simchowitz, Christopher Tosh, Akshay Krishnamurthy, Daniel J. Hsu, Thodoris Lykouris, Miroslav Dudík, Robert E. Schapire:
Bayesian decision-making under misspecified priors with applications to meta-learning. NeurIPS 2021: 26382-26394 - [c22]Juan C. Perdomo, Jack Umenberger, Max Simchowitz:
Stabilizing Dynamical Systems via Policy Gradient Methods. NeurIPS 2021: 29274-29286 - [i33]Andrew Wagenmaker, Max Simchowitz, Kevin G. Jamieson:
Task-Optimal Exploration in Linear Dynamical Systems. CoRR abs/2102.05214 (2021) - [i32]Max Simchowitz, Aleksandrs Slivkins:
Exploration and Incentives in Reinforcement Learning. CoRR abs/2103.00360 (2021) - [i31]Juan C. Perdomo, Max Simchowitz, Alekh Agarwal, Peter L. Bartlett:
Towards a Dimension-Free Understanding of Adaptive Linear Control. CoRR abs/2103.10620 (2021) - [i30]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) - [i29]Max Simchowitz, Christopher Tosh, Akshay Krishnamurthy, Daniel Hsu, Thodoris Lykouris, Miroslav Dudík, Robert E. Schapire:
Bayesian decision-making under misspecified priors with applications to meta-learning. CoRR abs/2107.01509 (2021) - [i28]Andrew Wagenmaker, Max Simchowitz, Kevin G. Jamieson:
Beyond No Regret: Instance-Dependent PAC Reinforcement Learning. CoRR abs/2108.02717 (2021) - [i27]Juan C. Perdomo, Jack Umenberger, Max Simchowitz:
Stabilizing Dynamical Systems via Policy Gradient Methods. CoRR abs/2110.06418 (2021) - [i26]Andrew Wagenmaker, Yifang Chen, Max Simchowitz, Simon S. Du, Kevin Jamieson:
First-Order Regret in Reinforcement Learning with Linear Function Approximation: A Robust Estimation Approach. CoRR abs/2112.03432 (2021) - 2020
- [c21]Mark Braverman, Elad Hazan, Max Simchowitz, Blake E. Woodworth:
The Gradient Complexity of Linear Regression. COLT 2020: 627-647 - [c20]Max Simchowitz, Karan Singh, Elad Hazan:
Improper Learning for Non-Stochastic Control. COLT 2020: 3320-3436 - [c19]Dylan J. Foster, Max Simchowitz:
Logarithmic Regret for Adversarial Online Control. ICML 2020: 3211-3221 - [c18]Chi Jin, Akshay Krishnamurthy, Max Simchowitz, Tiancheng Yu:
Reward-Free Exploration for Reinforcement Learning. ICML 2020: 4870-4879 - [c17]Esther Rolf, Max Simchowitz, Sarah Dean, Lydia T. Liu, Daniel Björkegren, Moritz Hardt, Joshua Blumenstock:
Balancing Competing Objectives with Noisy Data: Score-Based Classifiers for Welfare-Aware Machine Learning. ICML 2020: 8158-8168 - [c16]Max Simchowitz, Dylan J. Foster:
Naive Exploration is Optimal for Online LQR. ICML 2020: 8937-8948 - [c15]Kianté Brantley, Miroslav Dudík, Thodoris Lykouris, Sobhan Miryoosefi, Max Simchowitz, Aleksandrs Slivkins, Wen Sun:
Constrained episodic reinforcement learning in concave-convex and knapsack settings. NeurIPS 2020 - [c14]Zakaria Mhammedi, Dylan J. Foster, Max Simchowitz, Dipendra Misra, Wen Sun, Akshay Krishnamurthy, Alexander Rakhlin, John Langford:
Learning the Linear Quadratic Regulator from Nonlinear Observations. NeurIPS 2020 - [c13]Max Simchowitz:
Making Non-Stochastic Control (Almost) as Easy as Stochastic. NeurIPS 2020 - [i25]Max Simchowitz, Karan Singh, Elad Hazan:
Improper Learning for Non-Stochastic Control. CoRR abs/2001.09254 (2020) - [i24]Max Simchowitz, Dylan J. Foster:
Naive Exploration is Optimal for Online LQR. CoRR abs/2001.09576 (2020) - [i23]Chi Jin, Akshay Krishnamurthy, Max Simchowitz, Tiancheng Yu:
Reward-Free Exploration for Reinforcement Learning. CoRR abs/2002.02794 (2020) - [i22]Dylan J. Foster, Max Simchowitz:
Logarithmic Regret for Adversarial Online Control. CoRR abs/2003.00189 (2020) - [i21]Esther Rolf, Max Simchowitz, Sarah Dean, Lydia T. Liu, Daniel Björkegren, Moritz Hardt, Joshua Blumenstock:
Balancing Competing Objectives with Noisy Data: Score-Based Classifiers for Welfare-Aware Machine Learning. CoRR abs/2003.06740 (2020) - [i20]Kianté Brantley, Miroslav Dudík, Thodoris Lykouris, Sobhan Miryoosefi, Max Simchowitz, Aleksandrs Slivkins, Wen Sun:
Constrained episodic reinforcement learning in concave-convex and knapsack settings. CoRR abs/2006.05051 (2020) - [i19]Max Simchowitz:
Making Non-Stochastic Control (Almost) as Easy as Stochastic. CoRR abs/2006.05910 (2020) - [i18]Zakaria Mhammedi, Dylan J. Foster, Max Simchowitz, Dipendra Misra, Wen Sun, Akshay Krishnamurthy, Alexander Rakhlin, John Langford:
Learning the Linear Quadratic Regulator from Nonlinear Observations. CoRR abs/2010.03799 (2020)
2010 – 2019
- 2019
- [j1]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) - [c12]Max Simchowitz, Ross Boczar, Benjamin Recht:
Learning Linear Dynamical Systems with Semi-Parametric Least Squares. COLT 2019: 2714-2802 - [c11]Lydia T. Liu, Max Simchowitz, Moritz Hardt:
The Implicit Fairness Criterion of Unconstrained Learning. ICML 2019: 4051-4060 - [c10]Lydia T. Liu, Sarah Dean, Esther Rolf, Max Simchowitz, Moritz Hardt:
Delayed Impact of Fair Machine Learning. IJCAI 2019: 6196-6200 - [c9]Max Simchowitz, Kevin G. Jamieson:
Non-Asymptotic Gap-Dependent Regret Bounds for Tabular MDPs. NeurIPS 2019: 1151-1160 - [i17]Max Simchowitz, Ross Boczar, Benjamin Recht:
Learning Linear Dynamical Systems with Semi-Parametric Least Squares. CoRR abs/1902.00768 (2019) - [i16]Max Simchowitz, Kevin G. Jamieson:
Non-Asymptotic Gap-Dependent Regret Bounds for Tabular MDPs. CoRR abs/1905.03814 (2019) - [i15]Mark Braverman, Elad Hazan, Max Simchowitz, Blake E. Woodworth:
The gradient complexity of linear regression. CoRR abs/1911.02212 (2019) - [i14]Thodoris Lykouris, Max Simchowitz, Aleksandrs Slivkins, Wen Sun:
Corruption Robust Exploration in Episodic Reinforcement Learning. CoRR abs/1911.08689 (2019) - 2018
- [c8]Reinhard Heckel, Max Simchowitz, Kannan Ramchandran, Martin J. Wainwright:
Approximate ranking from pairwise comparisons. AISTATS 2018: 1057-1066 - [c7]Max Simchowitz, Horia Mania, Stephen Tu, Michael I. Jordan, Benjamin Recht:
Learning Without Mixing: Towards A Sharp Analysis of Linear System Identification. COLT 2018: 439-473 - [c6]Lydia T. Liu, Sarah Dean, Esther Rolf, Max Simchowitz, Moritz Hardt:
Delayed Impact of Fair Machine Learning. ICML 2018: 3156-3164 - [c5]Max Simchowitz, Ahmed El Alaoui, Benjamin Recht:
Tight query complexity lower bounds for PCA via finite sample deformed wigner law. STOC 2018: 1249-1259 - [i13]Reinhard Heckel, Max Simchowitz, Kannan Ramchandran, Martin J. Wainwright:
Approximate Ranking from Pairwise Comparisons. CoRR abs/1801.01253 (2018) - [i12]Max Simchowitz, Horia Mania, Stephen Tu, Michael I. Jordan, Benjamin Recht:
Learning Without Mixing: Towards A Sharp Analysis of Linear System Identification. CoRR abs/1802.08334 (2018) - [i11]Lydia T. Liu, Sarah Dean, Esther Rolf, Max Simchowitz, Moritz Hardt:
Delayed Impact of Fair Machine Learning. CoRR abs/1803.04383 (2018) - [i10]Max Simchowitz, Ahmed El Alaoui, Benjamin Recht:
Tight Query Complexity Lower Bounds for PCA via Finite Sample Deformed Wigner Law. CoRR abs/1804.01221 (2018) - [i9]Max Simchowitz:
On the Randomized Complexity of Minimizing a Convex Quadratic Function. CoRR abs/1807.09386 (2018) - [i8]Max Simchowitz, Kevin G. Jamieson, Jordan W. Suchow, Thomas L. Griffiths:
Adaptive Sampling for Convex Regression. CoRR abs/1808.04523 (2018) - [i7]Lydia T. Liu, Max Simchowitz, Moritz Hardt:
Group calibration is a byproduct of unconstrained learning. CoRR abs/1808.10013 (2018) - [i6]Esther Rolf, David Fridovich-Keil, Max Simchowitz, Benjamin Recht, Claire J. Tomlin:
A Successive-Elimination Approach to Adaptive Robotic Sensing. CoRR abs/1809.10611 (2018) - 2017
- [c4]Max Simchowitz, Kevin G. Jamieson, Benjamin Recht:
The Simulator: Understanding Adaptive Sampling in the Moderate-Confidence Regime. COLT 2017: 1794-1834 - [i5]Max Simchowitz, Kevin G. Jamieson, Benjamin Recht:
The Simulator: Understanding Adaptive Sampling in the Moderate-Confidence Regime. CoRR abs/1702.05186 (2017) - [i4]Max Simchowitz, Ahmed El Alaoui, Benjamin Recht:
On the Gap Between Strict-Saddles and True Convexity: An Omega(log d) Lower Bound for Eigenvector Approximation. CoRR abs/1704.04548 (2017) - [i3]Jason D. Lee, Ioannis Panageas, Georgios Piliouras, Max Simchowitz, Michael I. Jordan, Benjamin Recht:
First-order Methods Almost Always Avoid Saddle Points. CoRR abs/1710.07406 (2017) - 2016
- [c3]Jason D. Lee, Max Simchowitz, Michael I. Jordan, Benjamin Recht:
Gradient Descent Only Converges to Minimizers. COLT 2016: 1246-1257 - [c2]Max Simchowitz, Kevin G. Jamieson, Benjamin Recht:
Best-of-K-bandits. COLT 2016: 1440-1489 - [c1]Stephen Tu, Ross Boczar, Max Simchowitz, Mahdi Soltanolkotabi, Ben Recht:
Low-rank Solutions of Linear Matrix Equations via Procrustes Flow. ICML 2016: 964-973 - [i2]Jason D. Lee, Max Simchowitz, Michael I. Jordan, Benjamin Recht:
Gradient Descent Converges to Minimizers. CoRR abs/1602.04915 (2016) - [i1]Max Simchowitz, Kevin G. Jamieson, Benjamin Recht:
Best-of-K Bandits. CoRR abs/1603.02752 (2016)
Coauthor Index
Benjamin Recht
aka: Ben Recht
aka: Ben Recht
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