Andrew Wagenmaker
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Andrew Wagenmaker ajwagen@cs.washington.edu |
I am a fifth-year Ph.D. student in Computer Science at the University of Washington working with Kevin Jamieson. My primary research interests are in the theoretical foundations of sequential decision-making, with a recent focus on pure exploration problems in reinforcement learning. I have spent time at Microsoft Research as well as the Simons Institute, and my work has been supported by an NSF Graduate Research Fellowship.
Previously, I completed a master's and bachelor's degree at the University of Michigan, both in Electrical Engineering. While at the University of Michigan, I worked with Raj Rao Nadakuditi and Necmiye Ozay.
Preprints
Instance-Optimality in Interactive Decision Making: Toward a Non-Asymptotic Theory
Andrew Wagenmaker and Dylan Foster
Preprint, 2023
Leveraging Offline Data in Online Reinforcement Learning
Andrew Wagenmaker and Aldo Pacchiano
Accepted to ICML, 2023
Publications
Instance-Dependent Near-Optimal Policy Identification in Linear MDPs via Online Experiment Design
Andrew Wagenmaker and Kevin Jamieson
NeurIPS, 2022
Active Learning with Safety Constraints
Romain Camilleri, Andrew Wagenmaker, Jamie Morgenstern, Lalit Jain, and Kevin Jamieson
NeurIPS, 2022
Reward-Free RL is No Harder Than Reward-Aware RL in Linear Markov Decision Processes
Andrew Wagenmaker, Yifang Chen, Max Simchowitz, Simon S. Du, and Kevin Jamieson
ICML, 2022
First-Order Regret in Reinforcement Learning with Linear Function
Approximation: A Robust Estimation Approach
Andrew Wagenmaker, Yifang Chen, Max Simchowitz, Simon S. Du, and Kevin Jamieson
ICML, 2022 (Long Talk) [Talk]
Beyond No Regret: Instance-Dependent PAC Reinforcement Learning
Andrew Wagenmaker, Max Simchowitz, and Kevin Jamieson
COLT, 2022 [Talk]
Best Arm Identification with Safety Constraints
Zhenlin Wang, Andrew Wagenmaker, and Kevin Jamieson
AISTATS, 2022
Task-Optimal Exploration in Linear Dynamical Systems
Andrew Wagenmaker, Max Simchowitz, and Kevin Jamieson
ICML, 2021 (Long Talk)
Experimental Design for Regret Minimization in Linear Bandits
Andrew Wagenmaker*, Julian Katz-Samuels*, and Kevin Jamieson
AISTATS, 2021
Active Learning for Identification of Linear Dynamical Systems
Andrew Wagenmaker and Kevin Jamieson
COLT, 2020 [Talk]
Robust Photometric Stereo via Dictionary Learning
Andrew Wagenmaker, Brian Moore, and Raj Rao Nadakuditi
IEEE Transactions on Computational Imaging, 2018
Robust Photometric Stereo Using Learned Image and Gradient Dictionaries
Andrew Wagenmaker, Brian Moore, and Raj Rao Nadakuditi
IEEE International Conference on Image Processing (ICIP), 2017
Robust Surface Reconstruction from Gradients via Adaptive Dictionary Regularization
Andrew Wagenmaker, Brian Moore, and Raj Rao Nadakuditi
IEEE International Conference on Image Processing (ICIP), 2017
A Bisimulation-Like Algorithm for Abstracting Control Systems
Andrew Wagenmaker and Necmiye Ozay
Allerton Conference on Communication, Control, and Computing, 2016