Publications

You can also find my articles on my Google Scholar profile.

Longer Writing

PhD Thesis: Statistical Complexity and Regret in Linear Control

Max Simchowitz. UC Berkeley Department of Electrical Engineering and Computer Science, 2021.

Selected Publications

Allen Z. Ren, Justin Lidard, Lars L. Ankile, Anthony Simeonov, Pulkit Agrawal, Anirudha Majumdar, Benjamin Burchfiel, Hongkai Dai, Max Simchowitz. ArXiv Preprint, 2024.

Diffusion Forcing: Next-token Prediction Meets Full-Sequence Diffusion

Boyuan Chen, Diego Marti Monso, Yilun Du, Max Simchowitz, Russ Tedrake, Vincent Sitzmann. Neurips, 2024.

Faster Algorithms for Growing Collision-Free Convex Polytopes in Robot Configuration Space

Peter Werner*, Thomas Cohn, Rebecca H. Jiang, Tim Niklas Seyde, Max Simchowitz, Russ Tedrake, Daniela Rus. ISRR, 2024.

Provable Guarantees for Generative Behavior Cloning: Bridging Low-Level Stability and High-Level Behavior

α: Adam Block, Ali Jadbabaie, Daniel Pfrommer, Max Simchowitz, Russ Tedrake. NeurIPS, 2023.

Do Differentiable Simulators Give Better Policy Gradients?

H.J. Terry Suh, Max Simchowitz, Kaiqing Zhang, Russ Tedrake. ICML, Outstanding Paper Award 2022.

Naive Exploration is Optimal for Online LQR

Max Simchowitz, Dylan Foster. ICML, 2020.

Improper Learning for Nonstochastic Control

Max Simchowitz, Karan Singh, Elad Hazan. COLT, 2020.

Learning Without Mixing: Towards A Sharp Analysis of Linear System Identification

Max Simchowitz, Horia Mania, Stephen Tu, Benjamin Recht, Michael I. Jordan. COLT, 2018.

Preprints

Is Linear Feedback on Smoothed Dynamics Sufficient for Stabilizing Contact-Rich Plans?

Yuki Shirai, Tong Zhao, H.J. Terry Suh, Huaijiang Zhu, Xinpei Ni, Jiuguang Wang, Max Simchowitz, Tao Pang. ArXiv Preprint, 2024.

Diffusion Policy Policy Optimization

Allen Z. Ren, Justin Lidard, Lars L. Ankile, Anthony Simeonov, Pulkit Agrawal, Anirudha Majumdar, Benjamin Burchfiel, Hongkai Dai, Max Simchowitz. ArXiv Preprint, 2024.

Adaptive Sampling for Convex Regression

Max Simchowitz, Kevin Jamieson, Jordan W. Suchow, Thomas L. Griffiths. Formerly R&R at Annals of Statistics, 2018.

2025

Non-Euclidean Motion Planning with Graphs of Geodesically-Convex Sets

Thomas Cohn, Mark Petersen, Max Simchowitz, Russ Tedrake. IJRR, 2025.

2024

Diffusion Forcing: Next-token Prediction Meets Full-Sequence Diffusion

Boyuan Chen, Diego Marti Monso, Yilun Du, Max Simchowitz, Russ Tedrake, Vincent Sitzmann. Neurips, 2024.

Faster Algorithms for Growing Collision-Free Convex Polytopes in Robot Configuration Space

Peter Werner*, Thomas Cohn, Rebecca H. Jiang, Tim Niklas Seyde, Max Simchowitz, Russ Tedrake, Daniela Rus. ISRR, 2024.

Constrained Bimanual Planning with Analytic Inverse Kinematics

Thomas Cohn, Seiji Shaw, Max Simchowitz, Russ Tedrake. ICRA, Best Paper Finalist 2024.

Fleet Policy Learning via Weight Merging and An Application to Robotic Tool-Use

Lirui Wang, Kaiqing Zhang, Allan Zhou, Max Simchowitz, Russ Tedrake. ICLR, 2024.

Butterfly Effects of SGD Noise: Error Amplification in Behavior Cloning and Autoregression

Adam Block, Dylan J. Foster, Akshay Krishnamurthy, Max Simchowitz, Cyril Zhang. ICLR, 2024.

2023

RePo: Resilient Model-Based Reinforcement Learning by Regularizing Posterior Predictability

Chuning Zhu, Max Simchowitz, Siri Gadipudi, Abhishek Gupta. NeurIPS, Spotlight 2023.

Smoothed Online Learning for Prediction in Piecewise Affine Systems

Adam Block, Max Simchowitz, Russ Tedrake. NeurIPS, Spotlight 2023.

Provable Guarantees for Generative Behavior Cloning: Bridging Low-Level Stability and High-Level Behavior

α: Adam Block, Ali Jadbabaie, Daniel Pfrommer, Max Simchowitz, Russ Tedrake. NeurIPS, 2023.

Exploration and Incentives in Reinforcement Learning

Max Simchowitz, Aleksandrs Slivkins. Operations Research, 2023.

Non-Euclidean Motion Planning with Graphs of Geodesically-Convex Sets

Thomas Cohn, Mark Petersen, Max Simchowitz, Russ Tedrake. RSS, Best Paper Finalist 2023.

The Power of Learned Locally Linear Models for Nonlinear Policy Optimization

Daniel Pfrommer^* , Max Simchowitz^* , Tyler Westenbroek, Nikolai Matni, Stephen Tu. ICML, 2023.

Imitating Complex Trajectories: Bridging Low-Level Stability and High-Level Behavior

α: Adam Block , Daniel Pfrommer, Max Simchowitz. Frontiers4LCD Workshop, ICML, 2023.

Statistical Learning under Heterogenous Distribution Shift

Max Simchowitz, Anurag Ajay, Pulkit Agrawal, Akshay Krishnamurthy. ICML, 2023.

Oracle-Efficient Smoothed Online Learning for Piecewise Continuous Decision Making

α: Adam Block, Sasha Rakhlin, Max Simchowitz. COLT, 2023.

Tackling Combinatorial Distribution Shift: A Matrix Completion Perspective

Max Simchowitz, Abhishek Gupta, Kaiqing Zhang. COLT, 2023.

Learning to Extrapolate: A Transductive Approach

Aviv Netanyahu, Abhishek Gupta, Max Simchowitz, Kaiqing Zhang, Pulkit Agrawal. ICLR, 2023.

2022

Pathologies and Challenges of Using Differentiable Simulators in Policy Optimization for Contact-Rich Manipulation

H.J. Terry Suh, Max Simchowitz, Kaiqing Zhang, Russ Tedrake. ICRA 2022 Workshop: Reinforcement Learning for Contact-Rich Manipulation, 2022.

Learning to Extrapolate: A Transductive Approach

Aviv Netanyahu, Abhishek Gupta, Max Simchowitz, Kaiqing Zhang, Pulkit Agrawal. NeurIPS 2022 Workshop on Distribution Shifts (DistShift), 2022.

Globally Convergent Policy Search over Dynamic Filters for Output Estimation

Jack Umenberger^*, Max Simchowitz^*, Juan C. Perdomo, Kaiqing Zhang, Russ Tedrake. NeurIPS, 2022.

Efficient and Near-Optimal Smoothed Online Learning for Generalized Linear Functions

α: Adam Block, Max Simchowitz. NeurIPS, 2022.

Do Differentiable Simulators Give Better Policy Gradients?

H.J. Terry Suh, Max Simchowitz, Kaiqing Zhang, Russ Tedrake. ICML, Outstanding Paper Award 2022.

Reward-Free RL is No Harder Than Reward-Aware RL in Linear Markov Decision Processes

Andrew Wagenmaker, Yifang Chen, Max Simchowitz, Simon S. Du, Kevin Jamieson. ICML, Spotlight 2022.

First-Order Regret in Reinforcement Learning with Linear Function Approximation: A Robust Estimation Approach

Andrew Wagenmaker, Yifang Chen, Max Simchowitz, Simon S. Du, Kevin Jamieson.. ICML, Oral 2022.

Beyond No Regret: Instance-Dependent PAC Reinforcement Learning

Andrew Wagenmaker, Max Simchowitz, Kevin Jamieson. COLT, 2022.

Older Publications

Online Control of Unknown Time-Varying Dynamical Systems

Edgar Minasyan, Paula Gradu, Max Simchowitz, Elad Hazan. NeurIPS, 2021.

Stabilizing Dynamical Systems via Policy Gradient Methods

Juan C. Perdomo, Jack Umenberger, Max Simchowitz. NeurIPS, 2021.

Bayesian decision-making under misspecified priors with applications to meta-learning

Max Simchowitz, Christopher Tosh, Akshay Krishnamurthy, Daniel Hsu, Thodoris Lykouris, Miroslav Dudík, Robert E. Schapire. NeurIPS, Spotlight 2021.

On Stability of Nonlinear Receding Horizon Control: A Geometric Perspective

Tyler Westenbroek^*, Max Simchowitz^*, Michael I. Jordan, S. Shankar Sastry. CDC, 2021.

Task-Optimal Exploration in Linear Dynamical Systems

Andrew Wagenmaker, Max Simchowitz, Kevin Jamieson. COLT, 2021.

Corruption Robust Exploration in Episodic Reinforcement Learning

α: Thodoris Lykouris, Max Simchowitz, Aleksandrs Slivkins, Wen Sun. COLT, 2021.

Towards a Dimension-Free Understanding of Adaptive Linear Control

Juan C. Perdomo, Max Simchowitz, Alekh Agarwal, Peter Bartlett. COLT, 2021.

Learning a Linear Quadratic Regular from Nonlinear Observations

Zak Mhammedi, Dylan Foster, Max Simchowitz, Dipendra Misra, Akshay Krishnamurthy, Sasha Rakhlin, John Langford. NeurIPS, 2020.

Making Non-Stochastic Control (Almost) as Easy as Stochastic

Max Simchowitz. NeurIPS, 2020.

Constrained episodic reinforcement learning in concave-convex and knapsack settings

α: Kiante Brantley, Miroslav Dudik, Thodoris Lykouris, Sobhan Miryoosefi, Max Simchowitz, Aleksandrs Slivkins, Wen Sun. NeurIPS, 2020.

Reward-Free Exploration for Reinforcement Learning

α: Chi Jin, Akshay Krishnamurthy, Max Simchowitz, Tiancheng Yu. ICML, 2020.

Naive Exploration is Optimal for Online LQR

Max Simchowitz, Dylan Foster. ICML, 2020.

Logarithmic Regret for Online Control with Adversarial Disturbances

Dylan Foster, Max Simchowitz. ICML, 2020.

Balancing Competing Objectives with Noisy Data: Score-Based Classifiers for Welfare-Aware Machine Learning

Esther Rolf, Max Simchowitz, Sarah Dean, Lydia T. Liu, Daniel Bjorkegren, Moritz Hardt, Joshua Blumenstock. ICML, 2020.

Improper Learning for Nonstochastic Control

Max Simchowitz, Karan Singh, Elad Hazan. COLT, 2020.

The gradient complexity of linear regression

α: Mark Braverman, Elad Hazan, Max Simchowitz, Blake Woodworth. COLT, 2020.

A Successive-Elimination Approach to Adaptive Robotic Source Seeking

Esther Rolf, David Fridovich-Keil, Max Simchowitz, Benjamin Recht, Claire Tomlin. Transactions on Robotics, 2020.

First-order Methods Almost Always Avoid Saddle Points

Jason D. Lee, Ioannis Panageas, Georgios Piliouras, Max Simchowitz, Michael I. Jordan, Benjamin Recht. Mathematical Programming (pg. 1-27), 2019.

Learning Linear Dynamical Systems with Semi-Parametric Least Squares

Max Simchowitz, Ross Boczar, Benjamin Recht. COLT, 2019.

The Implicit Fairness Criterion of Unconstrained Learning

Lydia Liu, Max Simchowitz, Moritz Hardt. ICML, 2019.

Non-Asymptotic Gap-Dependent Regret Bounds for Tabular MDPs”

Max Simchowitz, Kevin Jamieson. NeurIPS, 2019.

First-order Methods Almost Always Avoid Saddle Points

Jason D. Lee, Ioannis Panageas, Georgios Piliouras, Max Simchowitz, Michael I. Jordan, Benjamin Recht. Mathematical Programming (pg. 1-27), 2019.

Balancing Competing Objectives for Welfare-Aware Machine Learning with Imperfect Data

Esther Rolf, Max Simchowitz, Sarah Dean, Lydia T. Liu, Daniel Björkegren, Moritz Hardt, Joshua Blumenstock. The Neural Information Processing Systems Joint Workshop on AI for Social Good, Best Paper Award 2019.

Approximate Ranking from Pairwise Comparisons

Reinhard Heckel, Max Simchowitz, Kannan Ramchandran, Martin J. Wainwright. AISTATS, 2018.

Tight Query Complexity Lower Bounds for PCA via Finite Sample Deformed Wigner Law

Max Simchowitz, Ahmed El Alaoui, Benjamin Recht. STOC, 2018.

Learning Without Mixing: Towards A Sharp Analysis of Linear System Identification

Max Simchowitz, Horia Mania, Stephen Tu, Benjamin Recht, Michael I. Jordan. COLT, 2018.

Delayed Impact of Fair Machine Learning

Lydia T. Liu, Sarah Dean, Esther Rolf, Max Simchowitz, Moritz Hardt. ICML, Best Paper Award 2018.

The Simulator: Towards a Richer Understanding of Adaptive Sampling in the Moderate-Confidence Regime

Max Simchowitz, Kevin Jamieson, and Benjamin Recht. COLT, 2017.

Low-rank Solutions of Linear Matrix Equations via Procrustes Flow

Stephen Tu, Ross Boczar, Max Simchowitz, Mahdi Soltanolkotabi, Benjamin Recht. ICML, 2016.

Gradient Descent Converges to Minimizers

Jason D. Lee, Max Simchowitz, Michael I. Jordan, and Benjamin Recht. COLT, 2016.

Best-of-K Bandits

Max Simchowitz, Kevin Jamieson, and Benjamin Recht. COLT, 2016.

Miscellaneous

On the Randomized Complexity of Minimizing a Convex Quadratic Function

Max Simchowitz. Arxiv, 2018.

On the Gap Between Strict-Saddles and True Convexity: An Omega(log d) Lower Bound for Eigenvector Approximation

Max Simchowitz, Ahmed El Alaoui, Benjamin Recht. Arxiv, 2017.