Andrew Wagenmaker

39 papers A* 12A 2Journal 24

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	RoboReward: General-Purpose Vision-Language Reward Models for Robotics. CoRR Tony Lee, Andrew Wagenmaker, Karl Pertsch, Percy Liang, Sergey Levine, Chelsea Finn
2025	A*	`conf`	Behavioral Exploration: Learning to Explore via In-Context Adaptation. ICML Andrew Wagenmaker, Zhiyuan Zhou, Sergey Levine
2025	J	`jnl`	Behavioral Exploration: Learning to Explore via In-Context Adaptation. CoRR Andrew Wagenmaker, Zhiyuan Zhou, Sergey Levine
2025	J	`jnl`	Posterior Behavioral Cloning: Pretraining BC Policies for Efficient RL Finetuning. CoRR Andrew Wagenmaker, Perry Dong, Raymond Tsao, Chelsea Finn, Sergey Levine
2025	J	`jnl`	Robust Finetuning of Vision-Language-Action Robot Policies via Parameter Merging. CoRR Yajat Yadav, Zhiyuan Zhou, Andrew Wagenmaker, Karl Pertsch, Sergey Levine
2025	J	`jnl`	Steering Your Diffusion Policy with Latent Space Reinforcement Learning. CoRR Andrew Wagenmaker, Mitsuhiko Nakamoto, Yunchu Zhang, Seohong Park, Waleed Yagoub, Anusha Nagabandi, Abhishek Gupta, Sergey Levine
2024	—	`—`	A Theory of Active Learning in Dynamic Environments Andrew Wagenmaker
2024	A*	`conf`	ASID: Active Exploration for System Identification in Robotic Manipulation. ICLR Marius Memmel, Andrew Wagenmaker, Chuning Zhu, Dieter Fox, Abhishek Gupta
2024	J	`jnl`	ASID: Active Exploration for System Identification in Robotic Manipulation. CoRR Marius Memmel, Andrew Wagenmaker, Chuning Zhu, Patrick Yin, Dieter Fox, Abhishek Gupta
2024	A*	`conf`	Active learning of neural population dynamics using two-photon holographic optogenetics. NeurIPS Andrew Wagenmaker, Lu Mi, Marton Rozsa, Matthew S. Bull, Karel Svoboda, Kayvon Daie, Matthew D. Golub, Kevin Jamieson
2024	J	`jnl`	Active learning of neural population dynamics using two-photon holographic optogenetics. CoRR Andrew Wagenmaker, Lu Mi, Marton Rozsa, Matthew S. Bull, Karel Svoboda, Kayvon Daie, Matthew D. Golub, Kevin Jamieson
2024	A*	`conf`	Corruption-Robust Linear Bandits: Minimax Optimality and Gap-Dependent Misspecification. NeurIPS Haolin Liu, Artin Tajdini, Andrew Wagenmaker, Chen-Yu Wei
2024	J	`jnl`	Corruption-Robust Linear Bandits: Minimax Optimality and Gap-Dependent Misspecification. CoRR Haolin Liu, Artin Tajdini, Andrew Wagenmaker, Chen-Yu Wei
2024	A	`conf`	Fair Active Learning in Low-Data Regimes. UAI Romain Camilleri, Andrew Wagenmaker, Jamie Morgenstern, Lalit Jain, Kevin Jamieson
2024	A*	`conf`	Humor in AI: Massive Scale Crowd-Sourced Preferences and Benchmarks for Cartoon Captioning. NeurIPS Jifan Zhang, Lalit K. Jain, Yang Guo, Jiayi Chen, Kuan Lok Zhou, Siddharth Suresh, Andrew Wagenmaker, Scott Sievert, Timothy T. Rogers, Kevin Jamieson, Bob Mankoff, Robert Nowak
2024	J	`jnl`	Humor in AI: Massive Scale Crowd-Sourced Preferences and Benchmarks for Cartoon Captioning. CoRR Jifan Zhang, Lalit K. Jain, Yang Guo, Jiayi Chen, Kuan Lok Zhou, Siddharth Suresh, Andrew Wagenmaker, Scott Sievert, Timothy T. Rogers, Kevin Jamieson, Robert Mankoff, Robert Nowak
2024	A*	`conf`	Overcoming the Sim-to-Real Gap: Leveraging Simulation to Learn to Explore for Real-World RL. NeurIPS Andrew Wagenmaker, Kevin Huang, Liyiming Ke, Kevin Jamieson, Abhishek Gupta
2024	J	`jnl`	Overcoming the Sim-to-Real Gap: Leveraging Simulation to Learn to Explore for Real-World RL. CoRR Andrew Wagenmaker, Kevin Huang, Liyiming Ke, Byron Boots, Kevin Jamieson, Abhishek Gupta
2024	A*	`conf`	Sample Complexity Reduction via Policy Difference Estimation in Tabular Reinforcement Learning. NeurIPS Adhyyan Narang, Andrew Wagenmaker, Lillian J. Ratliff, Kevin Jamieson
2024	J	`jnl`	Sample Complexity Reduction via Policy Difference Estimation in Tabular Reinforcement Learning. CoRR Adhyyan Narang, Andrew Wagenmaker, Lillian J. Ratliff, Kevin Jamieson
2023	J	`jnl`	Fair Active Learning in Low-Data Regimes. CoRR Romain Camilleri, Andrew Wagenmaker, Jamie Morgenstern, Lalit Jain, Kevin Jamieson
2023	J	`jnl`	Instance-Optimality in Interactive Decision Making: Toward a Non-Asymptotic Theory. CoRR Andrew Wagenmaker, Dylan J. Foster
2023	A*	`conf`	Leveraging Offline Data in Online Reinforcement Learning. ICML Andrew Wagenmaker, Aldo Pacchiano
2023	A*	`conf`	Optimal Exploration for Model-Based RL in Nonlinear Systems. NeurIPS Andrew Wagenmaker, Guanya Shi, Kevin Jamieson
2023	J	`jnl`	Optimal Exploration for Model-Based RL in Nonlinear Systems. CoRR Andrew Wagenmaker, Guanya Shi, Kevin Jamieson
2022	A*	`conf`	Active Learning with Safety Constraints. NeurIPS Romain Camilleri, Andrew Wagenmaker, Jamie H. Morgenstern, Lalit Jain, Kevin Jamieson
2022	J	`jnl`	Active Learning with Safety Constraints. CoRR Romain Camilleri, Andrew Wagenmaker, Jamie Morgenstern, Lalit Jain, Kevin Jamieson
2022	A*	`conf`	Instance-Dependent Near-Optimal Policy Identification in Linear MDPs via Online Experiment Design. NeurIPS Andrew Wagenmaker, Kevin Jamieson
2022	J	`jnl`	Instance-Dependent Near-Optimal Policy Identification in Linear MDPs via Online Experiment Design. CoRR Andrew Wagenmaker, Kevin Jamieson
2022	J	`jnl`	Leveraging Offline Data in Online Reinforcement Learning. CoRR Andrew Wagenmaker, Aldo Pacchiano
2022	J	`jnl`	Reward-Free RL is No Harder Than Reward-Aware RL in Linear Markov Decision Processes. CoRR Andrew Wagenmaker, Yifang Chen, Max Simchowitz, Simon S. Du, Kevin Jamieson
2021	J	`jnl`	Best Arm Identification with Safety Constraints. CoRR Zhenlin Wang, Andrew Wagenmaker, Kevin Jamieson
2021	J	`jnl`	Beyond No Regret: Instance-Dependent PAC Reinforcement Learning. CoRR Andrew Wagenmaker, Max Simchowitz, Kevin Jamieson
2021	A	`conf`	Experimental Design for Regret Minimization in Linear Bandits. AISTATS Andrew Wagenmaker, Julian Katz-Samuels, Kevin Jamieson
2021	J	`jnl`	First-Order Regret in Reinforcement Learning with Linear Function Approximation: A Robust Estimation Approach. CoRR Andrew Wagenmaker, Yifang Chen, Max Simchowitz, Simon S. Du, Kevin Jamieson
2021	J	`jnl`	Task-Optimal Exploration in Linear Dynamical Systems. CoRR Andrew Wagenmaker, Max Simchowitz, Kevin Jamieson
2020	A*	`conf`	Active Learning for Identification of Linear Dynamical Systems. COLT Andrew Wagenmaker, Kevin Jamieson
2020	J	`jnl`	Active Learning for Identification of Linear Dynamical Systems. CoRR Andrew Wagenmaker, Kevin Jamieson
2020	J	`jnl`	Experimental Design for Regret Minimization in Linear Bandits. CoRR Andrew Wagenmaker, Julian Katz-Samuels, Kevin Jamieson