Adam Block

According to our database, Adam Block authored at least 18 papers between 2020 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

On the Performance of Empirical Risk Minimization with Smoothed Data.

Adam Block, Alexander Rakhlin, Abhishek Shetty

CoRR, 2024

Oracle-Efficient Differentially Private Learning with Public Data.

Adam Block, Mark Bun, Rathin Desai, Abhishek Shetty, Steven Wu

CoRR, 2024

2023

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

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

CoRR, 2023

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

Adam Block, Daniel Pfrommer, Max Simchowitz

CoRR, 2023

Efficient Model-Free Exploration in Low-Rank MDPs.

Zakaria Mhammedi, Adam Block, Dylan J. Foster, Alexander Rakhlin

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Smoothed Online Learning for Prediction in Piecewise Affine Systems.

Adam Block, Max Simchowitz, Russ Tedrake

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 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

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Oracle-Efficient Smoothed Online Learning for Piecewise Continuous Decision Making.

Adam Block, Max Simchowitz, Alexander Rakhlin

Proceedings of the Thirty Sixth Annual Conference on Learning Theory, 2023

The Sample Complexity of Approximate Rejection Sampling With Applications to Smoothed Online Learning.

Adam Block, Yury Polyanskiy

Proceedings of the Thirty Sixth Annual Conference on Learning Theory, 2023

2022

Intrinsic Dimension Estimation Using Wasserstein Distance.

Adam Block, Zeyu Jia, Yury Polyanskiy, Alexander Rakhlin

J. Mach. Learn. Res., 2022

Rate of convergence of the smoothed empirical Wasserstein distance.

Adam Block, Zeyu Jia, Yury Polyanskiy, Alexander Rakhlin

CoRR, 2022

Counterfactual Learning To Rank for Utility-Maximizing Query Autocompletion.

Adam Block, Rahul Kidambi, Daniel N. Hill, Thorsten Joachims, Inderjit S. Dhillon

Proceedings of the SIGIR '22: The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, Madrid, Spain, July 11, 2022

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

Adam Block, Max Simchowitz

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Smoothed Online Learning is as Easy as Statistical Learning.

Adam Block, Yuval Dagan, Noah Golowich, Alexander Rakhlin

Proceedings of the Conference on Learning Theory, 2-5 July 2022, London, UK., 2022

2021

Intrinsic Dimension Estimation.

Adam Block, Zeyu Jia, Yury Polyanskiy, Alexander Rakhlin

CoRR, 2021

Majorizing Measures, Sequential Complexities, and Online Learning.

Adam Block, Yuval Dagan, Alexander Rakhlin

Proceedings of the Conference on Learning Theory, 2021

2020

Fast Mixing of Multi-Scale Langevin Dynamics under the Manifold Hypothesis.

Adam Block, Youssef Mroueh, Alexander Rakhlin, Jerret Ross

CoRR, 2020

Generative Modeling with Denoising Auto-Encoders and Langevin Sampling.

Adam Block, Youssef Mroueh, Alexander Rakhlin

CoRR, 2020