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Publications & Preprints
The publications and preprints are sorted chronologically. For the most updated list, see my Google Scholar.
Publications
Is Best-of-N the Best of Them? Coverage, Scaling, and Optimality in Inference-Time Alignment
Audrey Huang, Adam Block, Qinghua Liu, Nan Jiang, Dylan J. Foster, Akshay Krishnamurthy. arxiv
ICML 2025
GaussMark: A Practical Approach for Structural Watermarking of Language Models
Adam Block, Ayush Sekhari, and Alexander Rakhlin. arxiv
ICML 2025
A Theory of Learning with Autoregressive Chain of Thought
Nirmit Joshi, Gal Vardi, Adam Block, Surbhi Goel, Zhiyuan Li, Theodor Misiakiewicz, Nathan Srebro. arxiv
COLT 2025
Computational-Statistical Tradeoffs at the Next-Token Prediction Barrier: Autoregressive and Imitation Learning under Misspecification
Dhruv Rohatgi, Adam Block, Audrey Huang, Akshay Krishnamurthy, and Dylan Foster. arxiv
COLT 2025
Self-Improvement in Language Models: The Sharpening Mechanism N
Audrey Huang, Adam Block, Dylan J. Foster, Dhruv Rohatgi, Cyril Zhang, Max Simchowitz, Jordan T. Ash, and Akshay Krishnamurthy [https:arxiv.orgabs2412.01951
ICLR 2025 (Oral)
Is Behavior Cloning All You Need? Understanding Horizon in Imitation Learning
Dylan Foster, Adam Block, and Dipendra Misra arxiv
Neurips 2024 (Spotlight)
Oracle-Efficient Differentially Private Learning with Public Data
Adam Block, Mark Bun, Rathin Desai, Abhishek Shetty, and Zhiwei Steven Wu. arxiv
Neurips 2024
On the Performance of Empirical Risk Minimization with Smoothed Data
Adam Block, Alexander Rakhlin, and Abhishek Shetty. arxiv
COLT 2024
Butterfly Effects of SGD Noise: Error Amplification in Behavior Cloning and Autoregression
Adam Block, Dylan J. Foster, Akshay Krishnamurthy, Max Simchowitz, and Cyril Zhang. arxiv
ICLR 2024
Smoothed Online Learning for Prediction in Piecewise Affine Systems
Adam Block, Max Simchowitz, and Russ Tedrake. arxiv
Neurips 2023 (Spotlight)
Efficient Model-Free Exploration in Low-Rank MDPs
Zak Mhammedi, Adam Block, Dylan Foster, and Alexander Rakhlin. arxiv
NeurIPS 2023
On the Imitation of Non-Markovian Demonstrations: From Low-Level Stability to High Level Planning
Adam Block, Ali Jadbabaie, Daniel Pfrommer, Max Simchowitz, and Russ Tedrake. arxiv
NeurIPS 2023
Workshop on Optimal Transport in Learning, Control, and Dynamical Systems, ICML 2023
Oracle-Efficient Smoothed Online Learning for Piecewise Continuous Decision Making
Adam Block, Max Simchowitz, and Alexander Rakhlin. COLT 2023. arxiv
The Sample Complexity of Approximate Rejection Sampling With Applications to Smoothed Online Learning
Adam Block and Yury Polyanskiy. COLT 2023. arxiv
Efficient and Near-Optimal Smoothed Online Learning for Generalized Linear Functions
Adam Block and Max Simchowitz. NeurIPS 2022. arxiv
Intrinsic Dimension Estimation using Wasserstein Distance
Adam Block, Zeyu Jia, Yury Polyanskiy, and Alexander Rakhlin. Journal of Machine Learning Research (Accepted 2022). arxiv
Smoothed Online Learning is as Easy as Statistical Learning
Adam Block, Yuval Dagan, Noah Golowich, and Alexander Rakhlin. COLT 2022. arxiv
Counterfactual Learning To Rank for Utility-Maximizing Query Autocompletion
Adam Block, Rahul Kidambi, Thorsten Joachims, Daniel N. Hill, and Inderjit S. Dhillon. SIGIR 2022. arxiv
Majorizing Measures, Sequential Complexities, and Online Learning
Adam Block, Yuval Dagan, and Alexander Rakhlin. COLT 2021. arxiv
Preprints
Small Loss Bounds for Online Learning Separated Function Classes: A Gaussian Process Perspective
Adam Block and Abhishek Shetty. arxiv
Rate of Convergence of the Smoothed empirical Wasserstein Distance
Adam Block, Zeyu Jia, Yury Polyanskiy, and Alexander Rakhlin. arxiv
Fast mixing of multi-scale langevin dynamics underthe manifold hypothesis
Adam Block, Youssef Mroueh, Jerret Ross, and Alexander Rakhlin. arxiv
Generative modeling with denoising auto-encoders and Langevin sampling
Adam Block, Youssef Mroueh, and Alexander Rakhlin. arxiv
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