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2020 – today
- 2024
[c11]Adam Block, Alexander Rakhlin, Abhishek Shetty:
On the Performance of Empirical Risk Minimization with Smoothed Data. COLT 2024: 596-629- [c10]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 - [i18]Adam Block, Mark Bun, Rathin Desai, Abhishek Shetty, Steven Wu:
Oracle-Efficient Differentially Private Learning with Public Data. CoRR abs/2402.09483 (2024) - [i17]Adam Block, Alexander Rakhlin, Abhishek Shetty:
On the Performance of Empirical Risk Minimization with Smoothed Data. CoRR abs/2402.14987 (2024) - [i16]Dylan J. Foster, Adam Block, Dipendra Misra:
Is Behavior Cloning All You Need? Understanding Horizon in Imitation Learning. CoRR abs/2407.15007 (2024) - [i15]Audrey Huang, Adam Block, Dylan J. Foster, Dhruv Rohatgi, Cyril Zhang, Max Simchowitz, Jordan T. Ash, Akshay Krishnamurthy:
Self-Improvement in Language Models: The Sharpening Mechanism. CoRR abs/2412.01951 (2024) - 2023
- [c9]Adam Block, Yury Polyanskiy:
The Sample Complexity of Approximate Rejection Sampling With Applications to Smoothed Online Learning. COLT 2023: 228-273 - [c8]Adam Block, Max Simchowitz, Alexander Rakhlin:
Oracle-Efficient Smoothed Online Learning for Piecewise Continuous Decision Making. COLT 2023: 1618-1665 - [c7]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 - [c6]Adam Block, Max Simchowitz, Russ Tedrake:
Smoothed Online Learning for Prediction in Piecewise Affine Systems. NeurIPS 2023 - [c5]Zakaria Mhammedi, Adam Block, Dylan J. Foster, Alexander Rakhlin:
Efficient Model-Free Exploration in Low-Rank MDPs. NeurIPS 2023 - [i14]Adam Block, Max Simchowitz, Russ Tedrake:
Smoothed Online Learning for Prediction in Piecewise Affine Systems. CoRR abs/2301.11187 (2023) - [i13]Adam Block, Yury Polyanskiy:
The Sample Complexity of Approximate Rejection Sampling with Applications to Smoothed Online Learning. CoRR abs/2302.04658 (2023) - [i12]Adam Block, Alexander Rakhlin, Max Simchowitz:
Oracle-Efficient Smoothed Online Learning for Piecewise Continuous Decision Making. CoRR abs/2302.05430 (2023) - [i11]Zakaria Mhammedi, Adam Block, Dylan J. Foster, Alexander Rakhlin:
Efficient Model-Free Exploration in Low-Rank MDPs. CoRR abs/2307.03997 (2023) - [i10]Adam Block, Daniel Pfrommer, Max Simchowitz:
Imitating Complex Trajectories: Bridging Low-Level Stability and High-Level Behavior. CoRR abs/2307.14619 (2023) - [i9]Adam Block, Dylan J. Foster, Akshay Krishnamurthy, Max Simchowitz, Cyril Zhang:
Butterfly Effects of SGD Noise: Error Amplification in Behavior Cloning and Autoregression. CoRR abs/2310.11428 (2023) - 2022
- [j1]Adam Block, Zeyu Jia, Yury Polyanskiy, Alexander Rakhlin:
Intrinsic Dimension Estimation Using Wasserstein Distance. J. Mach. Learn. Res. 23: 313:1-313:37 (2022) - [c4]Adam Block, Yuval Dagan, Noah Golowich, Alexander Rakhlin:
Smoothed Online Learning is as Easy as Statistical Learning. COLT 2022: 1716-1786 - [c3]Adam Block, Max Simchowitz:
Efficient and Near-Optimal Smoothed Online Learning for Generalized Linear Functions. NeurIPS 2022 - [c2]Adam Block, Rahul Kidambi, Daniel N. Hill, Thorsten Joachims, Inderjit S. Dhillon:
Counterfactual Learning To Rank for Utility-Maximizing Query Autocompletion. SIGIR 2022: 791-802 - [i8]Adam Block, Yuval Dagan, Noah Golowich, Alexander Rakhlin:
Smoothed Online Learning is as Easy as Statistical Learning. CoRR abs/2202.04690 (2022) - [i7]Adam Block, Rahul Kidambi, Daniel N. Hill, Thorsten Joachims, Inderjit S. Dhillon:
Counterfactual Learning To Rank for Utility-Maximizing Query Autocompletion. CoRR abs/2204.10936 (2022) - [i6]Adam Block, Zeyu Jia, Yury Polyanskiy, Alexander Rakhlin:
Rate of convergence of the smoothed empirical Wasserstein distance. CoRR abs/2205.02128 (2022) - [i5]Adam Block, Max Simchowitz:
Efficient and Near-Optimal Smoothed Online Learning for Generalized Linear Functions. CoRR abs/2205.13056 (2022) - 2021
- [c1]Adam Block, Yuval Dagan, Alexander Rakhlin:
Majorizing Measures, Sequential Complexities, and Online Learning. COLT 2021: 587-590 - [i4]Adam Block, Yuval Dagan, Sasha Rakhlin:
Majorizing Measures, Sequential Complexities, and Online Learning. CoRR abs/2102.01729 (2021) - [i3]Adam Block, Zeyu Jia, Yury Polyanskiy, Alexander Rakhlin:
Intrinsic Dimension Estimation. CoRR abs/2106.04018 (2021) - 2020
- [i2]Adam Block, Youssef Mroueh, Alexander Rakhlin:
Generative Modeling with Denoising Auto-Encoders and Langevin Sampling. CoRR abs/2002.00107 (2020) - [i1]Adam Block, Youssef Mroueh, Alexander Rakhlin, Jerret Ross:
Fast Mixing of Multi-Scale Langevin Dynamics under the Manifold Hypothesis. CoRR abs/2006.11166 (2020)
Coauthor Index
aka: Sasha Rakhlin
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