Adam Block

Publication Activity (10 Years)

Years Active: 2020-2024
Publications (10 Years): 29

Top Topics

Piecewise Continuous

Intrinsic Dimension

Learning To Rank

Top Venues

J. Mach. Learn. Res.

Publications

Adam Block, Dylan J. Foster, Akshay Krishnamurthy, Max Simchowitz, Cyril Zhang
Butterfly Effects of SGD Noise: Error Amplification in Behavior Cloning and Autoregression. ICLR (2024)
Adam Block, Mark Bun, Rathin Desai, Abhishek Shetty, Steven Wu
Oracle-Efficient Differentially Private Learning with Public Data. CoRR (2024)
Adam Block, Alexander Rakhlin, Abhishek Shetty
On the Performance of Empirical Risk Minimization with Smoothed Data. COLT (2024)
Adam Block, Alexander Rakhlin, Abhishek Shetty
On the Performance of Empirical Risk Minimization with Smoothed Data. CoRR (2024)
Dylan J. Foster, Adam Block, Dipendra Misra
Is Behavior Cloning All You Need? Understanding Horizon in Imitation Learning. CoRR (2024)
Adam Block, Max Simchowitz, Russ Tedrake
Smoothed Online Learning for Prediction in Piecewise Affine Systems. CoRR (2023)
Adam Block, Dylan J. Foster, Akshay Krishnamurthy, Max Simchowitz, Cyril Zhang
Butterfly Effects of SGD Noise: Error Amplification in Behavior Cloning and Autoregression. CoRR (2023)
Adam Block, Daniel Pfrommer, Max Simchowitz
Imitating Complex Trajectories: Bridging Low-Level Stability and High-Level Behavior. CoRR (2023)
Adam Block, Yury Polyanskiy
The Sample Complexity of Approximate Rejection Sampling With Applications to Smoothed Online Learning. COLT (2023)
Adam Block, Max Simchowitz, Russ Tedrake
Smoothed Online Learning for Prediction in Piecewise Affine Systems. NeurIPS (2023)
Adam Block, Max Simchowitz, Alexander Rakhlin
Oracle-Efficient Smoothed Online Learning for Piecewise Continuous Decision Making. COLT (2023)
Adam Block, Yury Polyanskiy
The Sample Complexity of Approximate Rejection Sampling with Applications to Smoothed Online Learning. CoRR (2023)
Adam Block, Alexander Rakhlin, Max Simchowitz
Oracle-Efficient Smoothed Online Learning for Piecewise Continuous Decision Making. CoRR (2023)
Zakaria Mhammedi, Adam Block, Dylan J. Foster, Alexander Rakhlin
Efficient Model-Free Exploration in Low-Rank MDPs. NeurIPS (2023)
Zakaria Mhammedi, Adam Block, Dylan J. Foster, Alexander Rakhlin
Efficient Model-Free Exploration in Low-Rank MDPs. CoRR (2023)
Adam Block, Ali Jadbabaie, Daniel Pfrommer, Max Simchowitz, Russ Tedrake
Provable Guarantees for Generative Behavior Cloning: Bridging Low-Level Stability and High-Level Behavior. NeurIPS (2023)
Adam Block, Zeyu Jia, Yury Polyanskiy, Alexander Rakhlin
Rate of convergence of the smoothed empirical Wasserstein distance. CoRR (2022)
Adam Block, Max Simchowitz
Efficient and Near-Optimal Smoothed Online Learning for Generalized Linear Functions. CoRR (2022)
Adam Block, Rahul Kidambi, Daniel N. Hill, Thorsten Joachims, Inderjit S. Dhillon
Counterfactual Learning To Rank for Utility-Maximizing Query Autocompletion. CoRR (2022)
Adam Block, Yuval Dagan, Noah Golowich, Alexander Rakhlin
Smoothed Online Learning is as Easy as Statistical Learning. COLT (2022)
Adam Block, Max Simchowitz
Efficient and Near-Optimal Smoothed Online Learning for Generalized Linear Functions. NeurIPS (2022)
Adam Block, Yuval Dagan, Noah Golowich, Alexander Rakhlin
Smoothed Online Learning is as Easy as Statistical Learning. CoRR (2022)
Adam Block, Zeyu Jia, Yury Polyanskiy, Alexander Rakhlin
Intrinsic Dimension Estimation Using Wasserstein Distance. J. Mach. Learn. Res. 23 (2022)
Adam Block, Rahul Kidambi, Daniel N. Hill, Thorsten Joachims, Inderjit S. Dhillon
Counterfactual Learning To Rank for Utility-Maximizing Query Autocompletion. SIGIR (2022)
Adam Block, Yuval Dagan, Sasha Rakhlin
Majorizing Measures, Sequential Complexities, and Online Learning. CoRR (2021)
Adam Block, Zeyu Jia, Yury Polyanskiy, Alexander Rakhlin
Intrinsic Dimension Estimation. CoRR (2021)
Adam Block, Yuval Dagan, Alexander Rakhlin
Majorizing Measures, Sequential Complexities, and Online Learning. COLT (2021)
Adam Block, Youssef Mroueh, Alexander Rakhlin
Generative Modeling with Denoising Auto-Encoders and Langevin Sampling. CoRR (2020)
Adam Block, Youssef Mroueh, Alexander Rakhlin, Jerret Ross
Fast Mixing of Multi-Scale Langevin Dynamics under the Manifold Hypothesis. CoRR (2020)