Preprints

Learning to Actively Learn: A Robust Approach, Jifan Zhang, Kevin Jamieson, Preprint. PDF

Adaptive Sampling for Convex Regression, Max Simchowitz, Kevin Jamieson, Jordan Suchow, Tom Griffiths, Preprint. PDF

Publications

Sample-Efficient Identification of High-Dimensional Antibiotic Synergy with the Normalized Diagonal Sampling Design, Jennifer Brennan, Lalit Jain, Sofia Garman, Ann E. Donnelly, Erik S. Wright, Kevin Jamieson, PLoS computational biology 2022. PDF

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

Instance-optimal PAC Algorithms for Contextual Bandits, Zhaoqi Li, Lillian Ratliff, Houssam Nassif, Kevin Jamieson, Lalit Jain, NeurIPS 2022. PDF

Active Learning with Safety Constraints, Romain Camilleri, Andrew Wagenmaker, Jamie Morgenstern, Lalit Jain, Kevin Jamieson, NeurIPS 2022. PDF

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

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

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

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

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

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

Selective Sampling for Online Best-arm Identification, Romain Camilleri, Zhihan Xiong, Maryam Fazel, Lalit Jain, Kevin Jamieson, NeurIPS 2021. PDF

Practical, Provably-Correct Interactive Learning in the Realizable Setting: The Power of True Believers, Julian Katz-Samuels, Blake Mason, Kevin Jamieson, Rob Nowak, NeurIPS 2021. PDF

Corruption Robust Active Learning, Yifang Chen, Simon Shaolei Du, Kevin Jamieson, NeurIPS 2021. PDF

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

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

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

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

Leveraging Post Hoc Context for Faster Learning in Bandit Settings with Applications in Robot-Assisted Feeding, Ethan K. Gordon, Sumegh Roychowdhury, Tapomayukh Bhattacharjee, Kevin Jamieson, Siddhartha S. Srinivasa, ICRA 2021. PDF

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

An Empirical Process Approach to the Union Bound: Practical Algorithms for Combinatorial and Linear Bandits, Julian Katz-Samuels, Lalit Jain, Zohar Karnin, Kevin Jamieson, NeurIPS 2020. PDF

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

Estimating the number and effect sizes of non-null hypotheses, Jennifer Brennan, Ramya Korlakai Vinayak and Kevin Jamieson, ICML 2020. PDF

Massively Parallel Hyperparameter Tuning, Liam Li, Kevin Jamieson, Afshin Rostamizadeh, Ekaterina Gonina, Moritz Hardt, Benjamin Recht, Ameet Talwalkar, MLSys 2020. PDF

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

Sequential Experimental Design for Transductive Linear Bandits, Tanner Fiez, Lalit Jain, Kevin Jamieson, Lillian Ratliff, NeurIPS 2019. PDF

Non-Asymptotic Gap-Dependent Regret Bounds for Tabular MDPs, Max Simchowitz, Kevin Jamieson, NeurIPS 2019. PDF

A New Perspective on Pool-Based Active Classification and False-Discovery Control, Lalit Jain, Kevin Jamieson, NeurIPS 2019. PDF

A Bandit Approach to Multiple Testing with False Discovery Control, Kevin Jamieson, Lalit Jain, NeurIPS, 2018. PDF

Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization, Lisha Li, Kevin Jamieson, Giulia DeSalvo, Afshin Rostamizadeh, Ameet Talwalkar, JMLR, 2018*. PDF

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

A framework for Multi-A(rmed)/B(andit) testing with online FDR control, Fanny Yang, Aaditya Ramdas, Kevin Jamieson, Martin J. Wainwright, NeurIPS, 2017. PDF

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

Hyperband: Bandit-Based Configuration Evaluation for Hyperparameter Optimization, Lisha Li, Kevin Jamieson, Giulia DeSalvo, Afshin Rostamizadeh, Ameet Talwalkar, ICLR, 2017. PDF

Comparing Human-Centric and Robot-Centric Sampling for Robot Deep Learning from Demonstrations, Michael Laskey, Caleb Chuck, Jonathan Lee, Jeffrey Mahler, Sanjay Krishnan, Kevin Jamieson, Anca Dragan, Ken Goldberg, International Conference on Robotics and Automation (ICRA), 2017. PDF

The Power of Adaptivity in Identifying Statistical Alternatives, Kevin Jamieson, Daniel Haas, Ben Recht, NeurIPS, 2016*. PDF

Finite Sample Prediction and Recovery Bounds for Ordinal Embedding, Lalit Jain, Kevin Jamieson, Robert Nowak, NeurIPS, 2016. PDF

Best-of-K Bandits, Max Simchowitz, Kevin Jamieson, Benjamin Recht, COLT, 2016. PDF

Non-stochastic Best Arm Identification and Hyperparameter Optimization, Kevin Jamieson, Ameet Talwalkar, AISTATS, 2016. PDF

Top Arm Identification in Multi-Armed Bandits with Batch Arm Pulls, Kwang-Sung Jun, Kevin Jamieson, Robert Nowak, Xiaojin Zhu, AISTATS, 2016. PDF

NEXT: A System for Real-World Development, Evaluation, and Application of Active Learning, Kevin Jamieson, Lalit Jain, Chris Fernandez, Nick Glattard, Robert Nowak, NeurIPS, 2015. PDF

The Analysis of Adaptive Data Collection Methods for Machine Learning, Kevin Jamieson, PhD Thesis, University of Wisconsin - Madison, March 2015. PDF

Sparse Dueling Bandits, Kevin Jamieson, Sumeet Katariya, Atul Deshpande, and Robert Nowak, AISTATS, 2015. PDF

Best-arm identification algorithms for multi-armed bandits in the fixed confidence setting, Kevin Jamieson and Robert Nowak, CISS, 2014. PDF

lil' UCB : An Optimal Exploration Algorithm for Multi-Armed Bandits, Kevin Jamieson, Matt Malloy, Robert Nowak, and Sebastien Bubeck, COLT, 2014. PDF

On Finding the Largest Mean Among Many, Kevin Jamieson, Matt Malloy, Robert Nowak, and Sebastien Bubeck, Asilomar, 2013. PDF

Query Complexity of Derivative-Free Optimization, Kevin Jamieson, Robert Nowak, and Ben Recht, Neural Information Processing Systems (NeurIPS), 2012. PDF (Extended version)

Active Ranking using Pairwise Comparisons, Kevin Jamieson and Robert Nowak, Neural Information Processing Systems (NeurIPS), 2011. PDF (Extended version)

Low-Dimensional Embedding using Adaptively Selected Ordinal Data Kevin Jamieson and Robert Nowak, Allerton Conference on Communication, Control, and Computing, 2011. PDF

Channel-Robust Classifiers, Hyrum S. Anderson, Maya R. Gupta, Eric Swanson, and Kevin Jamieson, IEEE Trans. on Signal Processing, 2010.

Training a support vector machine to classify signals in a real environment given clean training data, Kevin Jamieson, Maya R. Gupta, Eric Swanson and Hyrum S. Anderson, Proc. IEEE ICASSP, 2010.

Sequential Bayesian Estimation of the Probability of Detection for Tracking, Kevin Jamieson, Maya R Gupta, and David Krout, Proc. IEEE Conference on Information Fusion, 2009.