Andrew J. Wagenmaker

According to our database, Andrew J. Wagenmaker authored at least 24 papers between 2016 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

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

CoRR, 2024

Corruption-Robust Linear Bandits: Minimax Optimality and Gap-Dependent Misspecification.

Haolin Liu, Artin Tajdini, Andrew Wagenmaker, Chen-Yu Wei

CoRR, 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 Jamieson, Robert Mankoff, Robert Nowak

CoRR, 2024

Sample Complexity Reduction via Policy Difference Estimation in Tabular Reinforcement Learning.

Adhyyan Narang, Andrew Wagenmaker, Lillian J. Ratliff, Kevin G. Jamieson

CoRR, 2024

ASID: Active Exploration for System Identification in Robotic Manipulation.

Marius Memmel, Andrew Wagenmaker, Chuning Zhu, Patrick Yin, Dieter Fox, Abhishek Gupta

CoRR, 2024

ASID: Active Exploration for System Identification in Robotic Manipulation.

Marius Memmel, Andrew Wagenmaker, Chuning Zhu, Dieter Fox, Abhishek Gupta

Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023

Fair Active Learning in Low-Data Regimes.

Romain Camilleri, Andrew Wagenmaker, Jamie Morgenstern, Lalit Jain, Kevin Jamieson

CoRR, 2023

Optimal Exploration for Model-Based RL in Nonlinear Systems.

Andrew Wagenmaker, Guanya Shi, Kevin G. Jamieson

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Leveraging Offline Data in Online Reinforcement Learning.

Andrew Wagenmaker, Aldo Pacchiano

Proceedings of the International Conference on Machine Learning, 2023

Instance-Optimality in Interactive Decision Making: Toward a Non-Asymptotic Theory.

Andrew J. Wagenmaker, Dylan J. Foster

Proceedings of the Thirty Sixth Annual Conference on Learning Theory, 2023

2022

Active Learning with Safety Constraints.

Romain Camilleri, Andrew Wagenmaker, Jamie Morgenstern, Lalit Jain, Kevin G. Jamieson

CoRR, 2022

Instance-Dependent Near-Optimal Policy Identification in Linear MDPs via Online Experiment Design.

Andrew Wagenmaker, Kevin G. Jamieson

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Active Learning with Safety Constraints.

Romain Camilleri, Andrew Wagenmaker, Jamie H. Morgenstern, Lalit Jain, Kevin G. Jamieson

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Reward-Free RL is No Harder Than Reward-Aware RL in Linear Markov Decision Processes.

Andrew J. Wagenmaker, Yifang Chen, Max Simchowitz, Simon S. Du, Kevin G. Jamieson

Proceedings of the International Conference on Machine Learning, 2022

First-Order Regret in Reinforcement Learning with Linear Function Approximation: A Robust Estimation Approach.

Andrew J. Wagenmaker, Yifang Chen, Max Simchowitz, Simon S. Du, Kevin G. Jamieson

Proceedings of the International Conference on Machine Learning, 2022

Beyond No Regret: Instance-Dependent PAC Reinforcement Learning.

Andrew J. Wagenmaker, Max Simchowitz, Kevin Jamieson

Proceedings of the Conference on Learning Theory, 2-5 July 2022, London, UK., 2022

Best Arm Identification with Safety Constraints.

Zhenlin Wang, Andrew J. Wagenmaker, Kevin G. Jamieson

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021

Task-Optimal Exploration in Linear Dynamical Systems.

Andrew J. Wagenmaker, Max Simchowitz, Kevin G. Jamieson

Proceedings of the 38th International Conference on Machine Learning, 2021

Experimental Design for Regret Minimization in Linear Bandits.

Andrew Wagenmaker, Julian Katz-Samuels, Kevin G. Jamieson

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020

Active Learning for Identification of Linear Dynamical Systems.

Andrew Wagenmaker, Kevin G. Jamieson

Proceedings of the Conference on Learning Theory, 2020

2019

Robust Photometric Stereo via Dictionary Learning.

Andrew John Wagenmaker, Brian E. Moore, Raj Rao Nadakuditi

IEEE Trans. Computational Imaging, 2019

2017

Robust photometric stereo using learned image and gradient dictionaries.

Andrew J. Wagenmaker, Brian E. Moore, Raj Rao Nadakuditi

Proceedings of the 2017 IEEE International Conference on Image Processing, 2017

Robust surface reconstruction from gradients via adaptive dictionary regularization.

Andrew J. Wagenmaker, Brian E. Moore, Raj Rao Nadakuditi

Proceedings of the 2017 IEEE International Conference on Image Processing, 2017

2016

A bisimulation-like algorithm for abstracting control systems.

Andrew J. Wagenmaker, Necmiye Ozay

Proceedings of the 54th Annual Allerton Conference on Communication, 2016