Generative Modeling with Denoising Auto-Encoders and Langevin Sampling. - Adam Block; Youssef Mroueh; Alexander Rakhlin (CoRR, 2020)

Fast Mixing of Multi-Scale Langevin Dynamics under the Manifold Hypothesis. - Adam Block; Youssef Mroueh; Alexander Rakhlin; Jerret Ross (CoRR, 2020)

Majorizing Measures, Sequential Complexities, and Online Learning. - Adam Block; Yuval Dagan; Alexander Rakhlin (COLT, 2021)

Intrinsic Dimension Estimation. - Adam Block; Zeyu Jia; Yury Polyanskiy; Alexander Rakhlin (CoRR, 2021)

Smoothed Online Learning is as Easy as Statistical Learning. - Adam Block; Yuval Dagan; Noah Golowich; Alexander Rakhlin (COLT, 2022)

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

Counterfactual Learning To Rank for Utility-Maximizing Query Autocompletion. - Adam Block; Rahul Kidambi; Daniel N. Hill; Thorsten Joachims; Inderjit S. Dhillon (SIGIR, 2022)

Rate of convergence of the smoothed empirical Wasserstein distance. - Adam Block; Zeyu Jia; Yury Polyanskiy; Alexander Rakhlin (CoRR, 2022)

Intrinsic Dimension Estimation Using Wasserstein Distance. - Adam Block; Zeyu Jia; Yury Polyanskiy; Alexander Rakhlin (J. Mach. Learn. Res., 2022)

The Sample Complexity of Approximate Rejection Sampling With Applications to Smoothed Online Learning. - Adam Block; Yury Polyanskiy (COLT, 2023)

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

Efficient Model-Free Exploration in Low-Rank MDPs. - Zakaria Mhammedi; Adam Block; Dylan J. Foster; Alexander Rakhlin (NeurIPS, 2023)

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

On the Performance of Empirical Risk Minimization with Smoothed Data. - Adam Block; Alexander Rakhlin; Abhishek Shetty (COLT, 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)

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

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

A Theory of Learning with Autoregressive Chain of Thought. - Nirmit Joshi; Gal Vardi; Adam Block; Surbhi Goel; Zhiyuan Li 005; 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 (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)

GaussMark: A Practical Approach for Structural Watermarking of Language Models. - Adam Block; Ayush Sekhari; Alexander Rakhlin (CoRR, 2025)

Small Loss Bounds for Online Learning Separated Function Classes: A Gaussian Process Perspective. - Adam Block; Abhishek Shetty (CoRR, 2025)

Computational-Statistical Tradeoffs at the Next-Token Prediction Barrier: Autoregressive and Imitation Learning under Misspecification. - Dhruv Rohatgi; Adam Block; Audrey Huang; Akshay Krishnamurthy; Dylan J. Foster (CoRR, 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 008; Dylan J. Foster; Akshay Krishnamurthy (CoRR, 2025)