Jamieson, Kevin

34 publications

NeurIPS 2025 Adapting to Stochastic and Adversarial Losses in Episodic MDPs with Aggregate Bandit Feedback Shinji Ito, Kevin Jamieson, Haipeng Luo, Arnab Maiti, Taira Tsuchiya

COLT 2025 Efficient Near-Optimal Algorithm for Online Shortest Paths in Directed Acyclic Graphs with Bandit Feedback Against Adaptive Adversaries Arnab Maiti, Zhiyuan Fan, Kevin Jamieson, Lillian J. Ratliff, Gabriele Farina

ICML 2025 Learning to Incentivize in Repeated Principal-Agent Problems with Adversarial Agent Arrivals Junyan Liu, Arnab Maiti, Artin Tajdini, Kevin Jamieson, Lillian J. Ratliff

NeurIPS 2025 On the Universal near Optimality of Hedge in Combinatorial Settings Zhiyuan Fan, Arnab Maiti, Lillian J. Ratliff, Kevin Jamieson, Gabriele Farina

AISTATS 2024 A/B Testing and Best-Arm Identification for Linear Bandits with Robustness to Non-Stationarity Zhihan Xiong, Romain Camilleri, Maryam Fazel, Lalit Jain, Kevin Jamieson

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 Jamieson

NeurIPS 2024 CLIPLoss and Norm-Based Data Selection Methods for Multimodal Contrastive Learning Yiping Wang, Yifang Chen, Wendan Yan, Alex Fang, Wenjing Zhou, Kevin Jamieson, Simon Shaolei Du

UAI 2024 Fair Active Learning in Low-Data Regimes Romain Camilleri, Andrew Wagenmaker, Jamie Morgenstern, Lalit Jain, Kevin 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 Rogers, Kevin Jamieson, Robert Mankoff, Robert Nowak

DMLR 2024 LabelBench: A Comprehensive Framework for Benchmarking Adaptive Label-Efficient Learning Jifan Zhang, Yifang Chen, Gregory Canal, Arnav Mohanty Das, Gantavya Bhatt, Stephen Mussmann, Yinglun Zhu, Jeff Bilmes, Simon Shaolei Du, Kevin Jamieson, Robert D Nowak

AISTATS 2024 Near-Optimal Pure Exploration in Matrix Games: A Generalization of Stochastic Bandits & Dueling Bandits Arnab Maiti, Ross Boczar, Kevin Jamieson, Lillian Ratliff

NeurIPS 2024 Nearly Minimax Optimal Submodular Maximization with Bandit Feedback Artin Tajdini, Lalit Jain, Kevin Jamieson

AISTATS 2024 Optimal Exploration Is No Harder than Thompson Sampling Zhaoqi Li, Kevin Jamieson, Lalit Jain

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 Jamieson, Abhishek Gupta

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

ICML 2023 Improved Active Multi-Task Representation Learning via Lasso Yiping Wang, Yifang Chen, Kevin Jamieson, Simon Shaolei Du

AISTATS 2023 Instance-Dependent Sample Complexity Bounds for Zero-Sum Matrix Games Arnab Maiti, Kevin Jamieson, Lillian Ratliff

NeurIPSW 2023 LabelBench: A Comprehensive Framework for Benchmarking Adaptive Label-Efficient Learning Jifan Zhang, Yifang Chen, Gregory Canal, Arnav Mohanty Das, Gantavya Bhatt, Yinglun Zhu, Stephen Mussmann, Simon Shaolei Du, Jeff Bilmes, Kevin Jamieson, Robert D Nowak

AISTATS 2022 Best Arm Identification with Safety Constraints Zhenlin Wang, Andrew J. Wagenmaker, Kevin Jamieson

AISTATS 2022 Nearly Optimal Algorithms for Level Set Estimation Blake Mason, Lalit Jain, Subhojyoti Mukherjee, Romain Camilleri, Kevin Jamieson, Robert Nowak

ICML 2022 Active Multi-Task Representation Learning Yifang Chen, Kevin Jamieson, Simon Du

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

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

ICML 2022 Reward-Free RL Is No Harder than Reward-Aware RL in Linear Markov Decision Processes Andrew J Wagenmaker, Yifang Chen, Max Simchowitz, Simon Du, Kevin Jamieson

AISTATS 2021 Experimental Design for Regret Minimization in Linear Bandits Andrew Wagenmaker, Julian Katz-Samuels, Kevin Jamieson

ICML 2021 High-Dimensional Experimental Design and Kernel Bandits Romain Camilleri, Kevin Jamieson, Julian Katz-Samuels

ICML 2021 Improved Algorithms for Agnostic Pool-Based Active Classification Julian Katz-Samuels, Jifan Zhang, Lalit Jain, Kevin Jamieson

ICML 2021 Improved Corruption Robust Algorithms for Episodic Reinforcement Learning Yifang Chen, Simon Du, Kevin Jamieson

ICML 2021 Task-Optimal Exploration in Linear Dynamical Systems Andrew J Wagenmaker, Max Simchowitz, Kevin Jamieson

COLT 2020 Active Learning for Identification of Linear Dynamical Systems Andrew Wagenmaker, Kevin Jamieson

ICML 2020 Estimating the Number and Effect Sizes of Non-Null Hypotheses Jennifer Brennan, Ramya Korlakai Vinayak, Kevin Jamieson

AISTATS 2020 The True Sample Complexity of Identifying Good Arms Julian Katz-Samuels, Kevin Jamieson

ICML 2018 Firing Bandits: Optimizing Crowdfunding Lalit Jain, Kevin Jamieson

COLT 2017 The Simulator: Understanding Adaptive Sampling in the Moderate-Confidence Regime Max Simchowitz, Kevin Jamieson, Benjamin Recht