Adam Block - RankMe

47 papers A* 18Journal 29

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Partition Function Estimation under Bounded f-Divergence. CoRR Adam Block, Abhishek Shetty
2026	J	`jnl`	Revisiting the (Sub)Optimality of Best-of-N for Inference-Time Alignment. CoRR Ved Sriraman, Adam Block
2025	A*	`conf`	A Theory of Learning with Autoregressive Chain of Thought. COLT Nirmit Joshi, Gal Vardi, Adam Block, Surbhi Goel, Zhiyuan Li, Theodor Misiakiewicz, Nathan Srebro
2025	J	`jnl`	A Theory of Learning with Autoregressive Chain of Thought. CoRR Nirmit Joshi, Gal Vardi, Adam Block, Surbhi Goel, Zhiyuan Li, Theodor Misiakiewicz, Nathan Srebro
2025	A*	`conf`	Computational-Statistical Tradeoffs at the Next-Token Prediction Barrier: Autoregressive and Imitation Learning under Misspecification (extended abstract). COLT Dhruv Rohatgi, Adam Block, Audrey Huang, Akshay Krishnamurthy, Dylan J. Foster
2025	J	`jnl`	Computational-Statistical Tradeoffs at the Next-Token Prediction Barrier: Autoregressive and Imitation Learning under Misspecification. CoRR Dhruv Rohatgi, Adam Block, Audrey Huang, Akshay Krishnamurthy, Dylan J. Foster
2025	J	`jnl`	EMA Without the Lag: Bias-Corrected Iterate Averaging Schemes. CoRR Adam Block, Cyril Zhang
2025	A*	`conf`	GaussMark: A Practical Approach for Structural Watermarking of Language Models. ICML Adam Block, Alexander Rakhlin, Ayush Sekhari
2025	J	`jnl`	GaussMark: A Practical Approach for Structural Watermarking of Language Models. CoRR Adam Block, Ayush Sekhari, Alexander Rakhlin
2025	A*	`conf`	Is Best-of-N the Best of Them? Coverage, Scaling, and Optimality in Inference-Time Alignment. ICML Audrey Huang, Adam Block, Qinghua Liu, Nan Jiang, Akshay Krishnamurthy, Dylan J. Foster
2025	J	`jnl`	Is Best-of-N the Best of Them? Coverage, Scaling, and Optimality in Inference-Time Alignment. CoRR Audrey Huang, Adam Block, Qinghua Liu, Nan Jiang, Dylan J. Foster, Akshay Krishnamurthy
2025	J	`jnl`	MarkTune: Improving the Quality-Detectability Trade-off in Open-Weight LLM Watermarking. CoRR Yizhou Zhao, Zhiwei Steven Wu, Adam Block
2025	A*	`conf`	Self-Improvement in Language Models: The Sharpening Mechanism. ICLR Audrey Huang, Adam Block, Dylan J. Foster, Dhruv Rohatgi, Cyril Zhang, Max Simchowitz, Jordan T. Ash, Akshay Krishnamurthy
2025	J	`jnl`	Small Loss Bounds for Online Learning Separated Function Classes: A Gaussian Process Perspective. CoRR Adam Block, Abhishek Shetty
2025	J	`jnl`	The Coverage Principle: How Pre-Training Enables Post-Training. CoRR Fan Chen, Audrey Huang, Noah Golowich, Sadhika Malladi, Adam Block, Jordan T. Ash, Akshay Krishnamurthy, Dylan J. Foster
2024	A*	`conf`	Butterfly Effects of SGD Noise: Error Amplification in Behavior Cloning and Autoregression. ICLR Adam Block, Dylan J. Foster, Akshay Krishnamurthy, Max Simchowitz, Cyril Zhang
2024	A*	`conf`	Is Behavior Cloning All You Need? Understanding Horizon in Imitation Learning. NeurIPS Dylan J. Foster, Adam Block, Dipendra Misra
2024	J	`jnl`	Is Behavior Cloning All You Need? Understanding Horizon in Imitation Learning. CoRR Dylan J. Foster, Adam Block, Dipendra Misra
2024	A*	`conf`	On the Performance of Empirical Risk Minimization with Smoothed Data. COLT Adam Block, Alexander Rakhlin, Abhishek Shetty
2024	J	`jnl`	On the Performance of Empirical Risk Minimization with Smoothed Data. CoRR Adam Block, Alexander Rakhlin, Abhishek Shetty
2024	A*	`conf`	Oracle-Efficient Differentially Private Learning with Public Data. NeurIPS Adam Block, Mark Bun, Rathin Desai, Abhishek Shetty, Zhiwei Steven Wu
2024	J	`jnl`	Oracle-Efficient Differentially Private Learning with Public Data. CoRR Adam Block, Mark Bun, Rathin Desai, Abhishek Shetty, Zhiwei Steven Wu
2024	J	`jnl`	Self-Improvement in Language Models: The Sharpening Mechanism. CoRR Audrey Huang, Adam Block, Dylan J. Foster, Dhruv Rohatgi, Cyril Zhang, Max Simchowitz, Jordan T. Ash, Akshay Krishnamurthy
2023	J	`jnl`	Butterfly Effects of SGD Noise: Error Amplification in Behavior Cloning and Autoregression. CoRR Adam Block, Dylan J. Foster, Akshay Krishnamurthy, Max Simchowitz, Cyril Zhang
2023	A*	`conf`	Efficient Model-Free Exploration in Low-Rank MDPs. NeurIPS Zakaria Mhammedi, Adam Block, Dylan J. Foster, Alexander Rakhlin
2023	J	`jnl`	Efficient Model-Free Exploration in Low-Rank MDPs. CoRR Zakaria Mhammedi, Adam Block, Dylan J. Foster, Alexander Rakhlin
2023	J	`jnl`	Imitating Complex Trajectories: Bridging Low-Level Stability and High-Level Behavior. CoRR Adam Block, Daniel Pfrommer, Max Simchowitz
2023	A*	`conf`	Oracle-Efficient Smoothed Online Learning for Piecewise Continuous Decision Making. COLT Adam Block, Max Simchowitz, Alexander Rakhlin
2023	J	`jnl`	Oracle-Efficient Smoothed Online Learning for Piecewise Continuous Decision Making. CoRR Adam Block, Alexander Rakhlin, Max Simchowitz
2023	A*	`conf`	Provable Guarantees for Generative Behavior Cloning: Bridging Low-Level Stability and High-Level Behavior. NeurIPS Adam Block, Ali Jadbabaie, Daniel Pfrommer, Max Simchowitz, Russ Tedrake
2023	A*	`conf`	Smoothed Online Learning for Prediction in Piecewise Affine Systems. NeurIPS Adam Block, Max Simchowitz, Russ Tedrake
2023	J	`jnl`	Smoothed Online Learning for Prediction in Piecewise Affine Systems. CoRR Adam Block, Max Simchowitz, Russ Tedrake
2023	A*	`conf`	The Sample Complexity of Approximate Rejection Sampling With Applications to Smoothed Online Learning. COLT Adam Block, Yury Polyanskiy
2023	J	`jnl`	The Sample Complexity of Approximate Rejection Sampling with Applications to Smoothed Online Learning. CoRR Adam Block, Yury Polyanskiy
2022	A*	`conf`	Counterfactual Learning To Rank for Utility-Maximizing Query Autocompletion. SIGIR Adam Block, Rahul Kidambi, Daniel N. Hill, Thorsten Joachims, Inderjit S. Dhillon
2022	J	`jnl`	Counterfactual Learning To Rank for Utility-Maximizing Query Autocompletion. CoRR Adam Block, Rahul Kidambi, Daniel N. Hill, Thorsten Joachims, Inderjit S. Dhillon
2022	A*	`conf`	Efficient and Near-Optimal Smoothed Online Learning for Generalized Linear Functions. NeurIPS Adam Block, Max Simchowitz
2022	J	`jnl`	Efficient and Near-Optimal Smoothed Online Learning for Generalized Linear Functions. CoRR Adam Block, Max Simchowitz
2022	J	`jnl`	Intrinsic Dimension Estimation Using Wasserstein Distance. J. Mach. Learn. Res. Adam Block, Zeyu Jia, Yury Polyanskiy, Alexander Rakhlin
2022	J	`jnl`	Rate of convergence of the smoothed empirical Wasserstein distance. CoRR Adam Block, Zeyu Jia, Yury Polyanskiy, Alexander Rakhlin
2022	A*	`conf`	Smoothed Online Learning is as Easy as Statistical Learning. COLT Adam Block, Yuval Dagan, Noah Golowich, Alexander Rakhlin
2022	J	`jnl`	Smoothed Online Learning is as Easy as Statistical Learning. CoRR Adam Block, Yuval Dagan, Noah Golowich, Alexander Rakhlin
2021	J	`jnl`	Intrinsic Dimension Estimation. CoRR Adam Block, Zeyu Jia, Yury Polyanskiy, Alexander Rakhlin
2021	A*	`conf`	Majorizing Measures, Sequential Complexities, and Online Learning. COLT Adam Block, Yuval Dagan, Alexander Rakhlin
2021	J	`jnl`	Majorizing Measures, Sequential Complexities, and Online Learning. CoRR Adam Block, Yuval Dagan, Sasha Rakhlin
2020	J	`jnl`	Fast Mixing of Multi-Scale Langevin Dynamics under the Manifold Hypothesis. CoRR Adam Block, Youssef Mroueh, Alexander Rakhlin, Jerret Ross
2020	J	`jnl`	Generative Modeling with Denoising Auto-Encoders and Langevin Sampling. CoRR Adam Block, Youssef Mroueh, Alexander Rakhlin