Andrew Wagenmaker

Publication Activity (10 Years)

Years Active: 2020-2024
Publications (10 Years): 23

Top Topics

Linear Dynamical Systems

Function Approximation

Experimental Designs

Reinforcement Learning

Top Venues

Publications

Marius Memmel, Andrew Wagenmaker, Chuning Zhu, Patrick Yin, Dieter Fox, Abhishek Gupta
ASID: Active Exploration for System Identification in Robotic Manipulation. CoRR (2024)
Jifan Zhang, Lalit Jain, Yang Guo, Jiayi Chen, Kuan Lok Zhou, Siddharth Suresh, Andrew Wagenmaker, Scott Sievert, Timothy T. Rogers, Kevin Jamieson, Robert Mankoff, Robert Nowak
Humor in AI: Massive Scale Crowd-Sourced Preferences and Benchmarks for Cartoon Captioning. CoRR (2024)
Adhyyan Narang, Andrew Wagenmaker, Lillian J. Ratliff, Kevin G. Jamieson
Sample Complexity Reduction via Policy Difference Estimation in Tabular Reinforcement Learning. CoRR (2024)
Marius Memmel, Andrew Wagenmaker, Chuning Zhu, Dieter Fox, Abhishek Gupta
ASID: Active Exploration for System Identification in Robotic Manipulation. ICLR (2024)
Andrew Wagenmaker, Guanya Shi, Kevin Jamieson
Optimal Exploration for Model-Based RL in Nonlinear Systems. CoRR (2023)
Andrew Wagenmaker, Dylan J. Foster
Instance-Optimality in Interactive Decision Making: Toward a Non-Asymptotic Theory. CoRR (2023)
Romain Camilleri, Andrew Wagenmaker, Jamie Morgenstern, Lalit Jain, Kevin Jamieson
Fair Active Learning in Low-Data Regimes. CoRR (2023)
Andrew Wagenmaker, Guanya Shi, Kevin G. Jamieson
Optimal Exploration for Model-Based RL in Nonlinear Systems. NeurIPS (2023)
Andrew Wagenmaker, Aldo Pacchiano
Leveraging Offline Data in Online Reinforcement Learning. ICML (2023)
Andrew Wagenmaker, Kevin Jamieson
Instance-Dependent Near-Optimal Policy Identification in Linear MDPs via Online Experiment Design. CoRR (2022)
Romain Camilleri, Andrew Wagenmaker, Jamie Morgenstern, Lalit Jain, Kevin G. Jamieson
Active Learning with Safety Constraints. CoRR (2022)
Romain Camilleri, Andrew Wagenmaker, Jamie H. Morgenstern, Lalit Jain, Kevin G. Jamieson
Active Learning with Safety Constraints. NeurIPS (2022)
Andrew Wagenmaker, Yifang Chen, Max Simchowitz, Simon S. Du, Kevin Jamieson
Reward-Free RL is No Harder Than Reward-Aware RL in Linear Markov Decision Processes. CoRR (2022)
Andrew Wagenmaker, Aldo Pacchiano
Leveraging Offline Data in Online Reinforcement Learning. CoRR (2022)
Andrew Wagenmaker, Kevin G. Jamieson
Instance-Dependent Near-Optimal Policy Identification in Linear MDPs via Online Experiment Design. NeurIPS (2022)
Andrew Wagenmaker, Max Simchowitz, Kevin G. Jamieson
Beyond No Regret: Instance-Dependent PAC Reinforcement Learning. CoRR (2021)
Andrew Wagenmaker, Max Simchowitz, Kevin G. Jamieson
Task-Optimal Exploration in Linear Dynamical Systems. CoRR (2021)
Andrew Wagenmaker, Yifang Chen, Max Simchowitz, Simon S. Du, Kevin Jamieson
First-Order Regret in Reinforcement Learning with Linear Function Approximation: A Robust Estimation Approach. CoRR (2021)
Zhenlin Wang, Andrew Wagenmaker, Kevin G. Jamieson
Best Arm Identification with Safety Constraints. CoRR (2021)
Andrew Wagenmaker, Julian Katz-Samuels, Kevin G. Jamieson
Experimental Design for Regret Minimization in Linear Bandits. AISTATS (2021)
Andrew Wagenmaker, Kevin G. Jamieson
Active Learning for Identification of Linear Dynamical Systems. CoRR (2020)
Andrew Wagenmaker, Julian Katz-Samuels, Kevin G. Jamieson
Experimental Design for Regret Minimization in Linear Bandits. CoRR (2020)
Andrew Wagenmaker, Kevin G. Jamieson
Active Learning for Identification of Linear Dynamical Systems. COLT (2020)