A bisimulation-like algorithm for abstracting control systems. - Andrew J. Wagenmaker; Necmiye Ozay (Allerton, 2016)

Robust surface reconstruction from gradients via adaptive dictionary regularization. - Andrew J. Wagenmaker; Brian E. Moore; Raj Rao Nadakuditi (ICIP, 2017)

Robust photometric stereo using learned image and gradient dictionaries. - Andrew J. Wagenmaker; Brian E. Moore; Raj Rao Nadakuditi (ICIP, 2017)

Robust Photometric Stereo via Dictionary Learning. - Andrew John Wagenmaker; Brian E. Moore; Raj Rao Nadakuditi (IEEE Trans. Computational Imaging, 2019)

Active Learning for Identification of Linear Dynamical Systems. - Andrew Wagenmaker; Kevin G. Jamieson (COLT, 2020)

Experimental Design for Regret Minimization in Linear Bandits. - Andrew Wagenmaker; Julian Katz-Samuels; Kevin G. Jamieson (AISTATS, 2021)

Task-Optimal Exploration in Linear Dynamical Systems. - Andrew J. Wagenmaker; Max Simchowitz; Kevin G. Jamieson (ICML, 2021)

Best Arm Identification with Safety Constraints. - Zhenlin Wang; Andrew J. Wagenmaker; Kevin G. Jamieson (AISTATS, 2022)

Beyond No Regret: Instance-Dependent PAC Reinforcement Learning. - Andrew J. Wagenmaker; Max Simchowitz; Kevin Jamieson 001 (COLT, 2022)

First-Order Regret in Reinforcement Learning with Linear Function Approximation: A Robust Estimation Approach. - Andrew J. Wagenmaker; Yifang Chen 001; Max Simchowitz; Simon S. Du; Kevin G. Jamieson (ICML, 2022)

Reward-Free RL is No Harder Than Reward-Aware RL in Linear Markov Decision Processes. - Andrew J. Wagenmaker; Yifang Chen 001; Max Simchowitz; Simon S. Du; Kevin G. Jamieson (ICML, 2022)

Active Learning with Safety Constraints. - Romain Camilleri; Andrew Wagenmaker; Jamie H. Morgenstern; Lalit Jain; Kevin G. Jamieson (NeurIPS, 2022)

Instance-Dependent Near-Optimal Policy Identification in Linear MDPs via Online Experiment Design. - Andrew Wagenmaker; Kevin G. Jamieson (NeurIPS, 2022)

Instance-Optimality in Interactive Decision Making: Toward a Non-Asymptotic Theory. - Andrew J. Wagenmaker; Dylan J. Foster (COLT, 2023)

Leveraging Offline Data in Online Reinforcement Learning. - Andrew Wagenmaker; Aldo Pacchiano (ICML, 2023)

Optimal Exploration for Model-Based RL in Nonlinear Systems. - Andrew Wagenmaker; Guanya Shi; Kevin G. Jamieson (NeurIPS, 2023)

ASID: Active Exploration for System Identification in Robotic Manipulation. - Marius Memmel; Andrew Wagenmaker; Chuning Zhu; Dieter Fox; Abhishek Gupta 004 (ICLR, 2024)

Corruption-Robust Linear Bandits: Minimax Optimality and Gap-Dependent Misspecification. - Haolin Liu; Artin Tajdini; Andrew Wagenmaker; Chen-Yu Wei (NeurIPS, 2024)

Sample Complexity Reduction via Policy Difference Estimation in Tabular Reinforcement Learning. - Adhyyan Narang; Andrew Wagenmaker; Lillian J. Ratliff; Kevin G. Jamieson (NeurIPS, 2024)

Overcoming the Sim-to-Real Gap: Leveraging Simulation to Learn to Explore for Real-World RL. - Andrew Wagenmaker; Kevin Huang; Liyiming Ke; Kevin G. Jamieson; Abhishek Gupta 004 (NeurIPS, 2024)

Active learning of neural population dynamics using two-photon holographic optogenetics. - Andrew Wagenmaker; Lu Mi; Marton Rozsa; Matthew S. Bull; Karel Svoboda; Kayvon Daie; Matthew D. Golub; Kevin G. Jamieson (NeurIPS, 2024)

Humor in AI: Massive Scale Crowd-Sourced Preferences and Benchmarks for Cartoon Captioning. - Jifan Zhang; Lalit Jain; Yang Guo; Jiayi Chen; Kuan Lok Zhou; Siddharth Suresh; Andrew Wagenmaker; Scott Sievert; Timothy T. Rogers; Kevin G. Jamieson; Bob Mankoff; Robert Nowak 001 (NeurIPS, 2024)

Fair Active Learning in Low-Data Regimes. - Romain Camilleri; Andrew Wagenmaker; Jamie Morgenstern; Lalit Jain; Kevin Jamieson 001 (UAI, 2024)

ASID: Active Exploration for System Identification in Robotic Manipulation. - Marius Memmel; Andrew Wagenmaker; Chuning Zhu; Patrick Yin; Dieter Fox; Abhishek Gupta 004 (CoRR, 2024)

Overcoming the Sim-to-Real Gap: Leveraging Simulation to Learn to Explore for Real-World RL. - Andrew Wagenmaker; Kevin Huang; Liyiming Ke; Byron Boots; Kevin G. Jamieson; Abhishek Gupta 004 (CoRR, 2024)

A Theory of Active Learning in Dynamic Environments - Andrew Wagenmaker (2024)

Steering Your Diffusion Policy with Latent Space Reinforcement Learning. - Andrew Wagenmaker; Mitsuhiko Nakamoto; Yunchu Zhang; Seohong Park; Waleed Yagoub; Anusha Nagabandi; Abhishek Gupta 004; Sergey Levine (CoRR, 2025)

Behavioral Exploration: Learning to Explore via In-Context Adaptation. - Andrew Wagenmaker; Zhiyuan Zhou; Sergey Levine (CoRR, 2025)