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ORCIDAndrew J. Wagenmaker
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2020 – today
- 2024
[b1]Andrew Wagenmaker:
A Theory of Active Learning in Dynamic Environments. University of Washington, USA, 2024- [c22]Marius Memmel, Andrew Wagenmaker, Chuning Zhu, Dieter Fox, Abhishek Gupta:
ASID: Active Exploration for System Identification in Robotic Manipulation. ICLR 2024 - [c21]Haolin Liu, Artin Tajdini, Andrew Wagenmaker, Chen-Yu Wei:
Corruption-Robust Linear Bandits: Minimax Optimality and Gap-Dependent Misspecification. NeurIPS 2024 - [c20]Adhyyan Narang, Andrew Wagenmaker, Lillian J. Ratliff, Kevin G. Jamieson:
Sample Complexity Reduction via Policy Difference Estimation in Tabular Reinforcement Learning. NeurIPS 2024 - [c19]Andrew Wagenmaker, Kevin Huang, Liyiming Ke, Kevin G. Jamieson, Abhishek Gupta:
Overcoming the Sim-to-Real Gap: Leveraging Simulation to Learn to Explore for Real-World RL. NeurIPS 2024 - [c18]Andrew Wagenmaker, Lu Mi, Marton Rozsa, Matthew S. Bull, Karel Svoboda, Kayvon Daie, Matthew D. Golub, Kevin G. Jamieson:
Active learning of neural population dynamics using two-photon holographic optogenetics. NeurIPS 2024 - [c17]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:
Humor in AI: Massive Scale Crowd-Sourced Preferences and Benchmarks for Cartoon Captioning. NeurIPS 2024 - [c16]Romain Camilleri, Andrew Wagenmaker, Jamie Morgenstern, Lalit Jain, Kevin Jamieson:
Fair Active Learning in Low-Data Regimes. UAI 2024: 517-531 - [i22]Marius Memmel, Andrew Wagenmaker, Chuning Zhu, Patrick Yin, Dieter Fox, Abhishek Gupta:
ASID: Active Exploration for System Identification in Robotic Manipulation. CoRR abs/2404.12308 (2024) - [i21]Adhyyan Narang, Andrew Wagenmaker, Lillian J. Ratliff, Kevin G. Jamieson:
Sample Complexity Reduction via Policy Difference Estimation in Tabular Reinforcement Learning. CoRR abs/2406.06856 (2024) - [i20]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 abs/2406.10522 (2024) - [i19]Haolin Liu, Artin Tajdini, Andrew Wagenmaker, Chen-Yu Wei:
Corruption-Robust Linear Bandits: Minimax Optimality and Gap-Dependent Misspecification. CoRR abs/2410.07533 (2024) - [i18]Andrew Wagenmaker, Kevin Huang, Liyiming Ke
, Byron Boots, Kevin G. Jamieson, Abhishek Gupta:
Overcoming the Sim-to-Real Gap: Leveraging Simulation to Learn to Explore for Real-World RL. CoRR abs/2410.20254 (2024) - [i17]Andrew Wagenmaker, Lu Mi, Marton Rozsa, Matthew S. Bull, Karel Svoboda, Kayvon Daie, Matthew D. Golub, Kevin G. Jamieson:
Active learning of neural population dynamics using two-photon holographic optogenetics. CoRR abs/2412.02529 (2024) - 2023
- [c15]Andrew J. Wagenmaker, Dylan J. Foster:
Instance-Optimality in Interactive Decision Making: Toward a Non-Asymptotic Theory. COLT 2023: 1322-1472 - [c14]Andrew Wagenmaker, Aldo Pacchiano:
Leveraging Offline Data in Online Reinforcement Learning. ICML 2023: 35300-35338 - [c13]Andrew Wagenmaker, Guanya Shi, Kevin G. Jamieson:
Optimal Exploration for Model-Based RL in Nonlinear Systems. NeurIPS 2023 - [i16]Andrew Wagenmaker, Dylan J. Foster:
Instance-Optimality in Interactive Decision Making: Toward a Non-Asymptotic Theory. CoRR abs/2304.12466 (2023) - [i15]Andrew Wagenmaker, Guanya Shi, Kevin Jamieson:
Optimal Exploration for Model-Based RL in Nonlinear Systems. CoRR abs/2306.09210 (2023) - [i14]Romain Camilleri, Andrew Wagenmaker, Jamie Morgenstern, Lalit Jain, Kevin Jamieson:
Fair Active Learning in Low-Data Regimes. CoRR abs/2312.08559 (2023) - 2022
- [c12]Zhenlin Wang, Andrew J. Wagenmaker, Kevin G. Jamieson:
Best Arm Identification with Safety Constraints. AISTATS 2022: 9114-9146 - [c11]Andrew J. Wagenmaker, Max Simchowitz, Kevin Jamieson:
Beyond No Regret: Instance-Dependent PAC Reinforcement Learning. COLT 2022: 358-418 - [c10]Andrew J. Wagenmaker, Yifang Chen, Max Simchowitz, Simon S. Du, Kevin G. Jamieson:
First-Order Regret in Reinforcement Learning with Linear Function Approximation: A Robust Estimation Approach. ICML 2022: 22384-22429 - [c9]Andrew J. Wagenmaker, Yifang Chen, Max Simchowitz, Simon S. Du, Kevin G. Jamieson:
Reward-Free RL is No Harder Than Reward-Aware RL in Linear Markov Decision Processes. ICML 2022: 22430-22456 - [c8]Romain Camilleri, Andrew Wagenmaker, Jamie H. Morgenstern, Lalit Jain, Kevin G. Jamieson:
Active Learning with Safety Constraints. NeurIPS 2022 - [c7]Andrew Wagenmaker, Kevin G. Jamieson:
Instance-Dependent Near-Optimal Policy Identification in Linear MDPs via Online Experiment Design. NeurIPS 2022 - [i13]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 abs/2201.11206 (2022) - [i12]Romain Camilleri, Andrew Wagenmaker, Jamie Morgenstern, Lalit Jain, Kevin G. Jamieson:
Active Learning with Safety Constraints. CoRR abs/2206.11183 (2022) - [i11]Andrew Wagenmaker, Kevin Jamieson:
Instance-Dependent Near-Optimal Policy Identification in Linear MDPs via Online Experiment Design. CoRR abs/2207.02575 (2022) - [i10]Andrew Wagenmaker, Aldo Pacchiano:
Leveraging Offline Data in Online Reinforcement Learning. CoRR abs/2211.04974 (2022) - 2021
- [c6]Andrew Wagenmaker, Julian Katz-Samuels, Kevin G. Jamieson:
Experimental Design for Regret Minimization in Linear Bandits. AISTATS 2021: 3088-3096 - [c5]Andrew J. Wagenmaker, Max Simchowitz, Kevin G. Jamieson:
Task-Optimal Exploration in Linear Dynamical Systems. ICML 2021: 10641-10652 - [i9]Andrew Wagenmaker, Max Simchowitz, Kevin G. Jamieson:
Task-Optimal Exploration in Linear Dynamical Systems. CoRR abs/2102.05214 (2021) - [i8]Andrew Wagenmaker, Max Simchowitz, Kevin G. Jamieson:
Beyond No Regret: Instance-Dependent PAC Reinforcement Learning. CoRR abs/2108.02717 (2021) - [i7]Zhenlin Wang, Andrew Wagenmaker, Kevin G. Jamieson:
Best Arm Identification with Safety Constraints. CoRR abs/2111.12151 (2021) - [i6]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 abs/2112.03432 (2021) - 2020
- [c4]Andrew Wagenmaker, Kevin G. Jamieson:
Active Learning for Identification of Linear Dynamical Systems. COLT 2020: 3487-3582 - [i5]Andrew Wagenmaker, Kevin G. Jamieson:
Active Learning for Identification of Linear Dynamical Systems. CoRR abs/2002.00495 (2020) - [i4]Andrew Wagenmaker, Julian Katz-Samuels, Kevin G. Jamieson:
Experimental Design for Regret Minimization in Linear Bandits. CoRR abs/2011.00576 (2020) - no results
2010 – 2019
- 2019
- [j1]Andrew John Wagenmaker, Brian E. Moore, Raj Rao Nadakuditi:
Robust Photometric Stereo via Dictionary Learning. IEEE Trans. Computational Imaging 5(2): 212-227 (2019) - 2017
- [c3]Andrew J. Wagenmaker, Brian E. Moore, Raj Rao Nadakuditi:
Robust surface reconstruction from gradients via adaptive dictionary regularization. ICIP 2017: 1002-1006 - [c2]Andrew J. Wagenmaker, Brian E. Moore, Raj Rao Nadakuditi:
Robust photometric stereo using learned image and gradient dictionaries. ICIP 2017: 1457-1461 - [i3]Andrew J. Wagenmaker, Brian E. Moore, Raj Rao Nadakuditi:
Robust Photometric Stereo Using Learned Image and Gradient Dictionaries. CoRR abs/1710.00002 (2017) - [i2]Andrew J. Wagenmaker, Brian E. Moore, Raj Rao Nadakuditi:
Robust Surface Reconstruction from Gradients via Adaptive Dictionary Regularization. CoRR abs/1710.00230 (2017) - [i1]Andrew J. Wagenmaker, Brian E. Moore, Raj Rao Nadakuditi:
Robust Photometric Stereo via Dictionary Learning. CoRR abs/1710.08873 (2017) - 2016
- [c1]Andrew J. Wagenmaker, Necmiye Ozay:
A bisimulation-like algorithm for abstracting control systems. Allerton 2016: 569-576 - no results
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