Max Simchowitz

Publication Activity (10 Years)

Years Active: 2016-2023
Publications (10 Years): 95

Top Topics

Policy Gradient

Reinforcement Learning

Piecewise Continuous

Top Venues

Publications

Adam Block, Max Simchowitz, Russ Tedrake
Smoothed Online Learning for Prediction in Piecewise Affine Systems. CoRR (2023)
Max Simchowitz, Anurag Ajay, Pulkit Agrawal, Akshay Krishnamurthy
Statistical Learning under Heterogenous Distribution Shift. CoRR (2023)
Adam Block, Dylan J. Foster, Akshay Krishnamurthy, Max Simchowitz, Cyril Zhang
Butterfly Effects of SGD Noise: Error Amplification in Behavior Cloning and Autoregression. CoRR (2023)
Adam Block, Daniel Pfrommer, Max Simchowitz
Imitating Complex Trajectories: Bridging Low-Level Stability and High-Level Behavior. CoRR (2023)
Lirui Wang, Kaiqing Zhang, Allan Zhou, Max Simchowitz, Russ Tedrake
Fleet Policy Learning via Weight Merging and An Application to Robotic Tool-Use. CoRR (2023)
Thomas Cohn, Seiji Shaw, Max Simchowitz, Russ Tedrake
Constrained Bimanual Planning with Analytic Inverse Kinematics. CoRR (2023)
Daniel Pfrommer, Max Simchowitz, Tyler Westenbroek, Nikolai Matni, Stephen Tu
The Power of Learned Locally Linear Models for Nonlinear Policy Optimization. CoRR (2023)
Max Simchowitz, Abhishek Gupta, Kaiqing Zhang
Tackling Combinatorial Distribution Shift: A Matrix Completion Perspective. CoRR (2023)
Adam Block, Max Simchowitz, Russ Tedrake
Smoothed Online Learning for Prediction in Piecewise Affine Systems. NeurIPS (2023)
Adam Block, Max Simchowitz, Alexander Rakhlin
Oracle-Efficient Smoothed Online Learning for Piecewise Continuous Decision Making. COLT (2023)
Thomas Cohn, Mark Petersen, Max Simchowitz, Russ Tedrake
Non-Euclidean Motion Planning with Graphs of Geodesically-Convex Sets. CoRR (2023)
Thomas Cohn, Mark Petersen, Max Simchowitz, Russ Tedrake
Non-Euclidean Motion Planning with Graphs of Geodesically-Convex Sets. Robotics: Science and Systems (2023)
Adam Block, Alexander Rakhlin, Max Simchowitz
Oracle-Efficient Smoothed Online Learning for Piecewise Continuous Decision Making. CoRR (2023)
Chuning Zhu, Max Simchowitz, Siri Gadipudi, Abhishek Gupta
RePo: Resilient Model-Based Reinforcement Learning by Regularizing Posterior Predictability. CoRR (2023)
Max Simchowitz, Anurag Ajay, Pulkit Agrawal, Akshay Krishnamurthy
Statistical Learning under Heterogenous Distribution Shift. ICML (2023)
Aviv Netanyahu, Abhishek Gupta, Max Simchowitz, Kaiqing Zhang, Pulkit Agrawal
Learning to Extrapolate: A Transductive Approach. ICLR (2023)
Aviv Netanyahu, Abhishek Gupta, Max Simchowitz, Kaiqing Zhang, Pulkit Agrawal
Learning to Extrapolate: A Transductive Approach. CoRR (2023)
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)
Max Simchowitz, Abhishek Gupta, Kaiqing Zhang
Tackling Combinatorial Distribution Shift: A Matrix Completion Perspective. COLT (2023)
Daniel Pfrommer, Max Simchowitz, Tyler Westenbroek, Nikolai Matni, Stephen Tu
The Power of Learned Locally Linear Models for Nonlinear Policy Optimization. ICML (2023)
Chuning Zhu, Max Simchowitz, Siri Gadipudi, Abhishek Gupta
RePo: Resilient Model-Based Reinforcement Learning by Regularizing Posterior Predictability. NeurIPS (2023)
Edgar Minasyan, Paula Gradu, Max Simchowitz, Elad Hazan
Online Control of Unknown Time-Varying Dynamical Systems. CoRR (2022)
Jack Umenberger, Max Simchowitz, Juan C. Perdomo, Kaiqing Zhang, Russ Tedrake
Globally Convergent Policy Search for Output Estimation. NeurIPS (2022)
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)
Adam Block, Max Simchowitz
Efficient and Near-Optimal Smoothed Online Learning for Generalized Linear Functions. CoRR (2022)
H. J. Terry Suh, Max Simchowitz, Kaiqing Zhang, Russ Tedrake
Do Differentiable Simulators Give Better Policy Gradients? CoRR (2022)
Jack Umenberger, Max Simchowitz, Juan C. Perdomo, Kaiqing Zhang, Russ Tedrake
Globally Convergent Policy Search over Dynamic Filters for Output Estimation. CoRR (2022)
Adam Block, Max Simchowitz
Efficient and Near-Optimal Smoothed Online Learning for Generalized Linear Functions. NeurIPS (2022)
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 (2022)
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)
Hyung Ju Suh, Max Simchowitz, Kaiqing Zhang, Russ Tedrake
Do Differentiable Simulators Give Better Policy Gradients? ICML (2022)
Andrew J. Wagenmaker, Max Simchowitz, Kevin Jamieson
Beyond No Regret: Instance-Dependent PAC Reinforcement Learning. COLT (2022)
Andrew Wagenmaker, Max Simchowitz, Kevin G. Jamieson
Beyond No Regret: Instance-Dependent PAC Reinforcement Learning. CoRR (2021)
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)
Andrew Wagenmaker, Max Simchowitz, Kevin G. Jamieson
Task-Optimal Exploration in Linear Dynamical Systems. CoRR (2021)
Max Simchowitz, Aleksandrs Slivkins
Exploration and Incentives in Reinforcement Learning. CoRR (2021)
Tyler Westenbroek, Max Simchowitz, Michael I. Jordan, S. Shankar Sastry
On the Stability of Nonlinear Receding Horizon Control: A Geometric Perspective. CoRR (2021)
Edgar Minasyan, Paula Gradu, Max Simchowitz, Elad Hazan
Online Control of Unknown Time-Varying Dynamical Systems. NeurIPS (2021)
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) (2021)
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 (2021)
Juan C. Perdomo, Max Simchowitz, Alekh Agarwal, Peter L. Bartlett
Towards a Dimension-Free Understanding of Adaptive Linear Control. CoRR (2021)
Juan C. Perdomo, Jack Umenberger, Max Simchowitz
Stabilizing Dynamical Systems via Policy Gradient Methods. CoRR (2021)
Andrew J. Wagenmaker, Max Simchowitz, Kevin G. Jamieson
Task-Optimal Exploration in Linear Dynamical Systems. ICML (2021)
Thodoris Lykouris, Max Simchowitz, Alex Slivkins, Wen Sun
Corruption-robust exploration in episodic reinforcement learning. COLT (2021)
Juan C. Perdomo, Jack Umenberger, Max Simchowitz
Stabilizing Dynamical Systems via Policy Gradient Methods. NeurIPS (2021)
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 (2021)
Tyler Westenbroek, Max Simchowitz, Michael I. Jordan, S. Shankar Sastry
On the Stability of Nonlinear Receding Horizon Control: A Geometric Perspective. CDC (2021)
Juan C. Perdomo, Max Simchowitz, Alekh Agarwal, Peter L. Bartlett
Towards a Dimension-Free Understanding of Adaptive Linear Control. COLT (2021)
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 (2020)
Max Simchowitz, Karan Singh, Elad Hazan
Improper Learning for Non-Stochastic Control. COLT (2020)
Max Simchowitz, Dylan J. Foster
Naive Exploration is Optimal for Online LQR. CoRR (2020)
Max Simchowitz
Making Non-Stochastic Control (Almost) as Easy as Stochastic. CoRR (2020)
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 (2020)
Chi Jin, Akshay Krishnamurthy, Max Simchowitz, Tiancheng Yu
Reward-Free Exploration for Reinforcement Learning. CoRR (2020)
Chi Jin, Akshay Krishnamurthy, Max Simchowitz, Tiancheng Yu
Reward-Free Exploration for Reinforcement Learning. ICML (2020)
Dylan J. Foster, Max Simchowitz
Logarithmic Regret for Adversarial Online Control. ICML (2020)
Dylan J. Foster, Max Simchowitz
Logarithmic Regret for Adversarial Online Control. CoRR (2020)
Max Simchowitz
Making Non-Stochastic Control (Almost) as Easy as Stochastic. NeurIPS (2020)
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)
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)
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)
Max Simchowitz, Dylan J. Foster
Naive Exploration is Optimal for Online LQR. ICML (2020)
Max Simchowitz, Karan Singh, Elad Hazan
Improper Learning for Non-Stochastic Control. CoRR (2020)
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 (2020)
Mark Braverman, Elad Hazan, Max Simchowitz, Blake E. Woodworth
The Gradient Complexity of Linear Regression. COLT (2020)
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) (2019)
Max Simchowitz, Ross Boczar, Benjamin Recht
Learning Linear Dynamical Systems with Semi-Parametric Least Squares. CoRR (2019)
Thodoris Lykouris, Max Simchowitz, Aleksandrs Slivkins, Wen Sun
Corruption Robust Exploration in Episodic Reinforcement Learning. CoRR (2019)
Max Simchowitz, Ross Boczar, Benjamin Recht
Learning Linear Dynamical Systems with Semi-Parametric Least Squares. COLT (2019)
Mark Braverman, Elad Hazan, Max Simchowitz, Blake E. Woodworth
The gradient complexity of linear regression. CoRR (2019)
Max Simchowitz, Kevin G. Jamieson
Non-Asymptotic Gap-Dependent Regret Bounds for Tabular MDPs. CoRR (2019)
Max Simchowitz, Kevin G. Jamieson
Non-Asymptotic Gap-Dependent Regret Bounds for Tabular MDPs. NeurIPS (2019)
Lydia T. Liu, Sarah Dean, Esther Rolf, Max Simchowitz, Moritz Hardt
Delayed Impact of Fair Machine Learning. IJCAI (2019)
Lydia T. Liu, Max Simchowitz, Moritz Hardt
The Implicit Fairness Criterion of Unconstrained Learning. ICML (2019)
Lydia T. Liu, Max Simchowitz, Moritz Hardt
Group calibration is a byproduct of unconstrained learning. CoRR (2018)
Lydia T. Liu, Sarah Dean, Esther Rolf, Max Simchowitz, Moritz Hardt
Delayed Impact of Fair Machine Learning. ICML (2018)
Max Simchowitz, Horia Mania, Stephen Tu, Michael I. Jordan, Benjamin Recht
Learning Without Mixing: Towards A Sharp Analysis of Linear System Identification. COLT (2018)
Esther Rolf, David Fridovich-Keil, Max Simchowitz, Benjamin Recht, Claire J. Tomlin
A Successive-Elimination Approach to Adaptive Robotic Sensing. CoRR (2018)
Max Simchowitz, Ahmed El Alaoui, Benjamin Recht
Tight Query Complexity Lower Bounds for PCA via Finite Sample Deformed Wigner Law. CoRR (2018)
Max Simchowitz, Kevin G. Jamieson, Jordan W. Suchow, Thomas L. Griffiths
Adaptive Sampling for Convex Regression. CoRR (2018)
Max Simchowitz
On the Randomized Complexity of Minimizing a Convex Quadratic Function. CoRR (2018)
Lydia T. Liu, Sarah Dean, Esther Rolf, Max Simchowitz, Moritz Hardt
Delayed Impact of Fair Machine Learning. CoRR (2018)
Reinhard Heckel, Max Simchowitz, Kannan Ramchandran, Martin J. Wainwright
Approximate ranking from pairwise comparisons. AISTATS (2018)
Max Simchowitz, Horia Mania, Stephen Tu, Michael I. Jordan, Benjamin Recht
Learning Without Mixing: Towards A Sharp Analysis of Linear System Identification. CoRR (2018)
Reinhard Heckel, Max Simchowitz, Kannan Ramchandran, Martin J. Wainwright
Approximate Ranking from Pairwise Comparisons. CoRR (2018)
Max Simchowitz, Ahmed El Alaoui, Benjamin Recht
Tight query complexity lower bounds for PCA via finite sample deformed wigner law. STOC (2018)
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 (2017)
Max Simchowitz, Kevin G. Jamieson, Benjamin Recht
The Simulator: Understanding Adaptive Sampling in the Moderate-Confidence Regime. COLT (2017)
Jason D. Lee, Ioannis Panageas, Georgios Piliouras, Max Simchowitz, Michael I. Jordan, Benjamin Recht
First-order Methods Almost Always Avoid Saddle Points. CoRR (2017)
Max Simchowitz, Kevin G. Jamieson, Benjamin Recht
The Simulator: Understanding Adaptive Sampling in the Moderate-Confidence Regime. CoRR (2017)
Jason D. Lee, Max Simchowitz, Michael I. Jordan, Benjamin Recht
Gradient Descent Only Converges to Minimizers. COLT (2016)
Max Simchowitz, Kevin G. Jamieson, Benjamin Recht
Best-of-K Bandits. CoRR (2016)
Max Simchowitz, Kevin G. Jamieson, Benjamin Recht
Best-of-K-bandits. COLT (2016)
Jason D. Lee, Max Simchowitz, Michael I. Jordan, Benjamin Recht
Gradient Descent Converges to Minimizers. CoRR (2016)
Stephen Tu, Ross Boczar, Max Simchowitz, Mahdi Soltanolkotabi, Ben Recht
Low-rank Solutions of Linear Matrix Equations via Procrustes Flow. ICML (2016)