Block, Adam

20 publications

COLT 2025 A Theory of Learning with Autoregressive Chain of Thought Nirmit Joshi, Gal Vardi, Adam Block, Surbhi Goel, Zhiyuan Li, Theodor Misiakiewicz, Nathan Srebro

COLT 2025 Computational-Statistical Tradeoffs at the Next-Token Prediction Barrier: Autoregressive and Imitation Learning Under Misspecification (extended Abstract) Dhruv Rohatgi, Adam Block, Audrey Huang, Akshay Krishnamurthy, Dylan J. Foster

ICML 2025 GaussMark: A Practical Approach for Structural Watermarking of Language Models Adam Block, Alexander Rakhlin, Ayush Sekhari

ICML 2025 Is Best-of-N the Best of Them? Coverage, Scaling, and Optimality in Inference-Time Alignment Audrey Huang, Adam Block, Qinghua Liu, Nan Jiang, Akshay Krishnamurthy, Dylan J Foster

ICLR 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 2024 Butterfly Effects of SGD Noise: Error Amplification in Behavior Cloning and Autoregression Adam Block, Dylan J Foster, Akshay Krishnamurthy, Max Simchowitz, Cyril Zhang

NeurIPS 2024 Is Behavior Cloning All You Need? Understanding Horizon in Imitation Learning Dylan J. Foster, Adam Block, Dipendra Misra

COLT 2024 On the Performance of Empirical Risk Minimization with Smoothed Data Adam Block, Alexander Rakhlin, Abhishek Shetty

NeurIPS 2024 Oracle-Efficient Differentially Private Learning with Public Data Adam Block, Mark Bun, Rathin Desai, Abhishek Shetty, Zhiwei Steven Wu

NeurIPSW 2024 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

NeurIPS 2023 Efficient Model-Free Exploration in Low-Rank MDPs Zak Mhammedi, Adam Block, Dylan J Foster, Alexander Rakhlin

ICMLW 2023 On the Imitation of Non-Markovian Demonstrations: From Low-Level Stability to High-Level Planning Adam Block, Daniel Pfrommer, Max Simchowitz

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

NeurIPS 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

COLT 2023 The Sample Complexity of Approximate Rejection Sampling with Applications to Smoothed Online Learning Adam Block, Yury Polyanskiy

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

JMLR 2022 Intrinsic Dimension Estimation Using Wasserstein Distance Adam Block, Zeyu Jia, Yury Polyanskiy, Alexander Rakhlin

COLT 2022 Smoothed Online Learning Is as Easy as Statistical Learning Adam Block, Yuval Dagan, Noah Golowich, Alexander Rakhlin

COLT 2021 Majorizing Measures, Sequential Complexities, and Online Learning Adam Block, Yuval Dagan, Alexander Rakhlin