Gradient Descent Only Converges to Minimizers. - Jason D. Lee; Max Simchowitz; Michael I. Jordan; Benjamin Recht (COLT, 2016)

Best-of-K-bandits. - Max Simchowitz; Kevin G. Jamieson; Benjamin Recht (COLT, 2016)

Low-rank Solutions of Linear Matrix Equations via Procrustes Flow. - Stephen Tu; Ross Boczar; Max Simchowitz; Mahdi Soltanolkotabi; Ben Recht (ICML, 2016)

Gradient Descent Converges to Minimizers. - Jason D. Lee; Max Simchowitz; Michael I. Jordan; Benjamin Recht (CoRR, 2016)

The Simulator: Understanding Adaptive Sampling in the Moderate-Confidence Regime. - Max Simchowitz; Kevin G. Jamieson; Benjamin Recht (COLT, 2017)

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 (CoRR, 2017)

First-order Methods Almost Always Avoid Saddle Points. - Jason D. Lee; Ioannis Panageas; Georgios Piliouras; Max Simchowitz; Michael I. Jordan; Benjamin Recht (CoRR, 2017)

Approximate ranking from pairwise comparisons. - Reinhard Heckel; Max Simchowitz; Kannan Ramchandran; Martin J. Wainwright (AISTATS, 2018)

Learning Without Mixing: Towards A Sharp Analysis of Linear System Identification. - Max Simchowitz; Horia Mania; Stephen Tu; Michael I. Jordan; Benjamin Recht (COLT, 2018)

Delayed Impact of Fair Machine Learning. - Lydia T. Liu; Sarah Dean; Esther Rolf; Max Simchowitz; Moritz Hardt (ICML, 2018)

Tight query complexity lower bounds for PCA via finite sample deformed wigner law. - Max Simchowitz; Ahmed El Alaoui; Benjamin Recht (STOC, 2018)

On the Randomized Complexity of Minimizing a Convex Quadratic Function. - Max Simchowitz (CoRR, 2018)

Adaptive Sampling for Convex Regression. - Max Simchowitz; Kevin G. Jamieson; Jordan W. Suchow; Thomas L. Griffiths 001 (CoRR, 2018)

Group calibration is a byproduct of unconstrained learning. - Lydia T. Liu; Max Simchowitz; Moritz Hardt (CoRR, 2018)

A Successive-Elimination Approach to Adaptive Robotic Sensing. - Esther Rolf; David Fridovich-Keil; Max Simchowitz; Benjamin Recht; Claire J. Tomlin (CoRR, 2018)

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 T. Liu; Max Simchowitz; Moritz Hardt (ICML, 2019)

Non-Asymptotic Gap-Dependent Regret Bounds for Tabular MDPs. - Max Simchowitz; Kevin G. Jamieson (NeurIPS, 2019)

First-order methods almost always avoid strict saddle points. - Jason D. Lee; Ioannis Panageas; Georgios Piliouras; Max Simchowitz; Michael I. Jordan; Benjamin Recht (Math. Program., 2019)

The Gradient Complexity of Linear Regression. - Mark Braverman; Elad Hazan; Max Simchowitz; Blake E. Woodworth (COLT, 2020)

Improper Learning for Non-Stochastic Control. - Max Simchowitz; Karan Singh; Elad Hazan (COLT, 2020)

Logarithmic Regret for Adversarial Online Control. - Dylan J. Foster; Max Simchowitz (ICML, 2020)

Reward-Free Exploration for Reinforcement Learning. - Chi Jin 001; Akshay Krishnamurthy; Max Simchowitz; Tiancheng Yu (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 Björkegren; Moritz Hardt; Joshua Blumenstock (ICML, 2020)

Naive Exploration is Optimal for Online LQR. - Max Simchowitz; Dylan J. Foster (ICML, 2020)

Constrained episodic reinforcement learning in concave-convex and knapsack settings. - Kianté Brantley; Miroslav Dudík; Thodoris Lykouris; Sobhan Miryoosefi; Max Simchowitz; Aleksandrs Slivkins; Wen Sun 002 (NeurIPS, 2020)

Learning the Linear Quadratic Regulator from Nonlinear Observations. - Zakaria Mhammedi; Dylan J. Foster; Max Simchowitz; Dipendra Misra; Wen Sun 002; Akshay Krishnamurthy; Alexander Rakhlin; John Langford 001 (NeurIPS, 2020)

Making Non-Stochastic Control (Almost) as Easy as Stochastic. - Max Simchowitz (NeurIPS, 2020)

On the Stability of Nonlinear Receding Horizon Control: A Geometric Perspective. - Tyler Westenbroek; Max Simchowitz; Michael I. Jordan; S. Shankar Sastry (CDC, 2021)

Towards a Dimension-Free Understanding of Adaptive Linear Control. - Juan C. Perdomo; Max Simchowitz; Alekh Agarwal; Peter L. Bartlett (COLT, 2021)

Task-Optimal Exploration in Linear Dynamical Systems. - Andrew J. Wagenmaker; Max Simchowitz; Kevin G. Jamieson (ICML, 2021)

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 J. Hsu; Thodoris Lykouris; Miroslav Dudík; Robert E. Schapire (NeurIPS, 2021)

A Successive-Elimination Approach to Adaptive Robotic Source Seeking. - Esther Rolf; David Fridovich-Keil; Max Simchowitz; Benjamin Recht; Claire J. Tomlin (IEEE Trans. Robotics, 2021)

Statistical Complexity and Regret in Linear Control. - Max Simchowitz (2021)

Beyond No Regret: Instance-Dependent PAC Reinforcement Learning. - Andrew J. Wagenmaker; Max Simchowitz; Kevin Jamieson 001 (COLT, 2022)

Do Differentiable Simulators Give Better Policy Gradients? - Hyung Ju Terry Suh; Max Simchowitz; Kaiqing Zhang; Russ Tedrake (ICML, 2022)

First-Order Regret in Reinforcement Learning with Linear Function Approximation: A Robust Estimation Approach. - Andrew J. Wagenmaker; Yifang Chen 001; Max Simchowitz; Simon S. Du; Kevin G. Jamieson (ICML, 2022)

Reward-Free RL is No Harder Than Reward-Aware RL in Linear Markov Decision Processes. - Andrew J. Wagenmaker; Yifang Chen 001; Max Simchowitz; Simon S. Du; Kevin G. Jamieson (ICML, 2022)

Efficient and Near-Optimal Smoothed Online Learning for Generalized Linear Functions. - Adam Block; Max Simchowitz (NeurIPS, 2022)

Globally Convergent Policy Search for Output Estimation. - Jack Umenberger; Max Simchowitz; Juan C. Perdomo; Kaiqing Zhang; Russ Tedrake (NeurIPS, 2022)

Globally Convergent Policy Search over Dynamic Filters for Output Estimation. - Jack Umenberger; Max Simchowitz; Juan C. Perdomo; Kaiqing Zhang; Russ Tedrake (CoRR, 2022)

Oracle-Efficient Smoothed Online Learning for Piecewise Continuous Decision Making. - Adam Block; Max Simchowitz; Alexander Rakhlin (COLT, 2023)

Tackling Combinatorial Distribution Shift: A Matrix Completion Perspective. - Max Simchowitz; Abhishek Gupta 004; Kaiqing Zhang (COLT, 2023)

Learning to Extrapolate: A Transductive Approach. - Aviv Netanyahu; Abhishek Gupta 004; Max Simchowitz; Kaiqing Zhang; Pulkit Agrawal 001 (ICLR, 2023)

The Power of Learned Locally Linear Models for Nonlinear Policy Optimization. - Daniel Pfrommer; Max Simchowitz; Tyler Westenbroek; Nikolai Matni; Stephen Tu (ICML, 2023)

Statistical Learning under Heterogenous Distribution Shift. - Max Simchowitz; Anurag Ajay; Pulkit Agrawal 001; Akshay Krishnamurthy (ICML, 2023)

Provable Guarantees for Generative Behavior Cloning: Bridging Low-Level Stability and High-Level Behavior. - Adam Block; Ali Jadbabaie; Daniel Pfrommer; Max Simchowitz; Russ Tedrake (NeurIPS, 2023)

Smoothed Online Learning for Prediction in Piecewise Affine Systems. - Adam Block; Max Simchowitz; Russ Tedrake (NeurIPS, 2023)

RePo: Resilient Model-Based Reinforcement Learning by Regularizing Posterior Predictability. - Chuning Zhu; Max Simchowitz; Siri Gadipudi; Abhishek Gupta 004 (NeurIPS, 2023)

Non-Euclidean Motion Planning with Graphs of Geodesically-Convex Sets. - Thomas Cohn; Mark Petersen; Max Simchowitz; Russ Tedrake (Robotics: Science and Systems, 2023)

Imitating Complex Trajectories: Bridging Low-Level Stability and High-Level Behavior. - Adam Block; Daniel Pfrommer; Max Simchowitz (CoRR, 2023)

Fleet Policy Learning via Weight Merging and An Application to Robotic Tool-Use. - Lirui Wang; Kaiqing Zhang; Allan Zhou; Max Simchowitz; Russ Tedrake (CoRR, 2023)

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)

Robot Fleet Learning via Policy Merging. - Lirui Wang; Kaiqing Zhang; Allan Zhou; Max Simchowitz; Russ Tedrake (ICLR, 2024)

Constrained Bimanual Planning with Analytic Inverse Kinematics. - Thomas Cohn; Seiji Shaw; Max Simchowitz; Russ Tedrake (ICRA, 2024)

Diffusion Forcing: Next-token Prediction Meets Full-Sequence Diffusion. - Boyuan Chen 003; 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 Seyde; Max Simchowitz; Russ Tedrake; Daniela Rus (CoRR, 2024)

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 (CoRR, 2024)

Exploration and Incentives in Reinforcement Learning. - Max Simchowitz; Aleksandrs Slivkins (Oper. Res., 2024)

The title of the paper. - Max Simchowitz; Daniel Pfrommer; Ali Jadbabaie (COLT, 2025)

Self-Improvement in Language Models: The Sharpening Mechanism. - Audrey Huang; Adam Block; Dylan J. Foster; Dhruv Rohatgi; Cyril Zhang; Max Simchowitz; Jordan T. Ash; Akshay Krishnamurthy (ICLR, 2025)

Diffusion Policy Policy Optimization. - Allen Z. Ren; Justin Lidard; Lars Lien Ankile; Anthony Simeonov; Pulkit Agrawal 001; Anirudha Majumdar; Benjamin Burchfiel; Hongkai Dai; Max Simchowitz (ICLR, 2025)

Corruption-Robust Exploration in Episodic Reinforcement Learning. - Thodoris Lykouris; Max Simchowitz; Aleksandrs Slivkins; Wen Sun 002 (Math. Oper. Res., 2025)

History-Guided Video Diffusion. - Kiwhan Song; Boyuan Chen 003; Max Simchowitz; Yilun Du; Russ Tedrake; Vincent Sitzmann (CoRR, 2025)

The Pitfalls of Imitation Learning when Actions are Continuous. - Max Simchowitz; Daniel Pfrommer; Ali Jadbabaie (CoRR, 2025)

e3: Learning to Explore Enables Extrapolation of Test-Time Compute for LLMs. - Amrith Setlur; Matthew Y. R. Yang; Charlie Snell; Jeremy Greer; Ian Wu; Virginia Smith; Max Simchowitz; Aviral Kumar (CoRR, 2025)

A Test-Function Approach to Incremental Stability. - Daniel Pfrommer; Max Simchowitz; Ali Jadbabaie (CoRR, 2025)