Kevin G. Jamieson

Publication Activity (10 Years)

Years Active: 2009-2024
Publications (10 Years): 81

Top Topics

Active Learning

Parameter Optimization

Sample Complexity

Top Venues

Publications

Zhaoqi Li, Kevin G. Jamieson, Lalit Jain
Optimal Exploration is no harder than Thompson Sampling. AISTATS (2024)
Arnab Maiti, Ross Boczar, Kevin G. Jamieson, Lillian J. Ratliff
Near-Optimal Pure Exploration in Matrix Games: A Generalization of Stochastic Bandits & Dueling Bandits. AISTATS (2024)
Yifang Chen, Shuohang Wang, Ziyi Yang, Hiteshi Sharma, Nikos Karampatziakis, Donghan Yu, Kevin G. Jamieson, Simon Shaolei Du, Yelong Shen
Cost-Effective Proxy Reward Model Construction with On-Policy and Active Learning. CoRR (2024)
Yiping Wang, Yifang Chen, Wendan Yan, Kevin G. Jamieson, Simon Shaolei Du
Variance Alignment Score: A Simple But Tough-to-Beat Data Selection Method for Multimodal Contrastive Learning. CoRR (2024)
Adhyyan Narang, Andrew Wagenmaker, Lillian J. Ratliff, Kevin G. Jamieson
Sample Complexity Reduction via Policy Difference Estimation in Tabular Reinforcement Learning. CoRR (2024)
Gantavya Bhatt, Yifang Chen, Arnav Mohanty Das, Jifan Zhang, Sang T. Truong, Stephen Mussmann, Yinglun Zhu, Jeffrey A. Bilmes, Simon S. Du, Kevin G. Jamieson, Jordan T. Ash, Robert D. Nowak
An Experimental Design Framework for Label-Efficient Supervised Finetuning of Large Language Models. CoRR (2024)
Zhihan Xiong, Romain Camilleri, Maryam Fazel, Lalit Jain, Kevin G. Jamieson
A/B Testing and Best-arm Identification for Linear Bandits with Robustness to Non-stationarity. AISTATS (2024)
Gantavya Bhatt, Yifang Chen, Arnav Mohanty Das, Jifan Zhang, Sang T. Truong, Stephen Mussmann, Yinglun Zhu, Jeff A. Bilmes, Simon S. Du, Kevin G. Jamieson, Jordan T. Ash, Robert D. Nowak
An Experimental Design Framework for Label-Efficient Supervised Finetuning of Large Language Models. ACL (Findings) (2024)
Zhihan Xiong, Romain Camilleri, Maryam Fazel, Lalit Jain, Kevin G. Jamieson
A/B Testing and Best-arm Identification for Linear Bandits with Robustness to Non-stationarity. CoRR (2023)
Jifan Zhang, Yifang Chen, Gregory Canal, Stephen Mussmann, Yinglun Zhu, Simon Shaolei Du, Kevin G. Jamieson, Robert D. Nowak
LabelBench: A Comprehensive Framework for Benchmarking Label-Efficient Learning. CoRR (2023)
Shuai Li, Azarakhsh Keipour, Kevin G. Jamieson, Nicolas Hudson, Charles Swan, Kostas E. Bekris
Demonstrating Large-Scale Package Manipulation via Learned Metrics of Pick Success. Robotics: Science and Systems (2023)
Arnab Maiti, Kevin G. Jamieson, Lillian J. Ratliff
Instance-dependent Sample Complexity Bounds for Zero-sum Matrix Games. AISTATS (2023)
Zhaoqi Li, Kevin G. Jamieson, Lalit Jain
Optimal Exploration is no harder than Thompson Sampling. CoRR (2023)
Arnab Maiti, Ross Boczar, Kevin G. Jamieson, Lillian J. Ratliff
Query-Efficient Algorithms to Find the Unique Nash Equilibrium in a Two-Player Zero-Sum Matrix Game. CoRR (2023)
Arnab Maiti, Kevin G. Jamieson, Lillian J. Ratliff
Logarithmic Regret for Matrix Games against an Adversary with Noisy Bandit Feedback. CoRR (2023)
Arnab Maiti, Ross Boczar, Kevin G. Jamieson, Lillian J. Ratliff
Near-Optimal Pure Exploration in Matrix Games: A Generalization of Stochastic Bandits & Dueling Bandits. CoRR (2023)
Yifang Chen, Yingbing Huang, Simon S. Du, Kevin G. Jamieson, Guanya Shi
Active Representation Learning for General Task Space with Applications in Robotics. CoRR (2023)
Andrew Wagenmaker, Guanya Shi, Kevin G. Jamieson
Optimal Exploration for Model-Based RL in Nonlinear Systems. NeurIPS (2023)
Yifang Chen, Yingbing Huang, Simon S. Du, Kevin G. Jamieson, Guanya Shi
Active representation learning for general task space with applications in robotics. NeurIPS (2023)
Arnab Maiti, Kevin G. Jamieson, Lillian J. Ratliff
Instance-dependent Sample Complexity Bounds for Zero-sum Matrix Games. CoRR (2023)
Yiping Wang, Yifang Chen, Kevin G. Jamieson, Simon S. Du
Improved Active Multi-Task Representation Learning via Lasso. CoRR (2023)
Jennifer Brennan, Lalit Jain, Sofia Garman, Ann E. Donnelly, Erik Scott Wright, Kevin G. Jamieson
Sample-efficient identification of high-dimensional antibiotic synergy with a normalized diagonal sampling design. PLoS Comput. Biol. 18 (7) (2022)
Yifang Chen, Kevin G. Jamieson, Simon S. Du
Active Multi-Task Representation Learning. ICML (2022)
Blake Mason, Lalit Jain, Subhojyoti Mukherjee, Romain Camilleri, Kevin G. Jamieson, Robert D. Nowak
Nearly Optimal Algorithms for Level Set Estimation. AISTATS (2022)
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)
Romain Camilleri, Andrew Wagenmaker, Jamie Morgenstern, Lalit Jain, Kevin G. Jamieson
Active Learning with Safety Constraints. CoRR (2022)
Romain Camilleri, Andrew Wagenmaker, Jamie H. Morgenstern, Lalit Jain, Kevin G. Jamieson
Active Learning with Safety Constraints. NeurIPS (2022)
Zhaoqi Li, Lillian J. Ratliff, Houssam Nassif, Kevin G. Jamieson, Lalit Jain
Instance-optimal PAC Algorithms for Contextual Bandits. CoRR (2022)
Zhaoqi Li, Lillian J. Ratliff, Houssam Nassif, Kevin G. Jamieson, Lalit Jain
Instance-optimal PAC Algorithms for Contextual Bandits. NeurIPS (2022)
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)
Andrew Wagenmaker, Kevin G. Jamieson
Instance-Dependent Near-Optimal Policy Identification in Linear MDPs via Online Experiment Design. NeurIPS (2022)
Zhenlin Wang, Andrew J. Wagenmaker, Kevin G. Jamieson
Best Arm Identification with Safety Constraints. AISTATS (2022)
Andrew Wagenmaker, Max Simchowitz, Kevin G. Jamieson
Beyond No Regret: Instance-Dependent PAC Reinforcement Learning. CoRR (2021)
Andrew Wagenmaker, Max Simchowitz, Kevin G. Jamieson
Task-Optimal Exploration in Linear Dynamical Systems. CoRR (2021)
Zhenlin Wang, Andrew Wagenmaker, Kevin G. Jamieson
Best Arm Identification with Safety Constraints. CoRR (2021)
Andrew Wagenmaker, Julian Katz-Samuels, Kevin G. Jamieson
Experimental Design for Regret Minimization in Linear Bandits. AISTATS (2021)
Andrew J. Wagenmaker, Max Simchowitz, Kevin G. Jamieson
Task-Optimal Exploration in Linear Dynamical Systems. ICML (2021)
Romain Camilleri, Julian Katz-Samuels, Kevin G. Jamieson
High-Dimensional Experimental Design and Kernel Bandits. CoRR (2021)
Julian Katz-Samuels, Blake Mason, Kevin G. Jamieson, Robert Nowak
Practical, Provably-Correct Interactive Learning in the Realizable Setting: The Power of True Believers. NeurIPS (2021)
Romain Camilleri, Zhihan Xiong, Maryam Fazel, Lalit Jain, Kevin G. Jamieson
Selective Sampling for Online Best-arm Identification. NeurIPS (2021)
Yifang Chen, Simon S. Du, Kevin G. Jamieson
Corruption Robust Active Learning. NeurIPS (2021)
Julian Katz-Samuels, Kevin G. Jamieson
The True Sample Complexity of Identifying Good Arms. AISTATS (2020)
Lalit Jain, Kevin G. Jamieson
A New Perspective on Pool-Based Active Classification and False-Discovery Control. CoRR (2020)
Andrew Wagenmaker, Kevin G. Jamieson
Active Learning for Identification of Linear Dynamical Systems. CoRR (2020)
Laurel J. Orr, Samuel K. Ainsworth, Kevin G. Jamieson, Walter Cai, Magdalena Balazinska, Dan Suciu
Mosaic: A Sample-Based Database System for Open World Query Processing. CIDR (2020)
Andrew Wagenmaker, Julian Katz-Samuels, Kevin G. Jamieson
Experimental Design for Regret Minimization in Linear Bandits. CoRR (2020)
Andrew Wagenmaker, Kevin G. Jamieson
Active Learning for Identification of Linear Dynamical Systems. COLT (2020)
Julian Katz-Samuels, Lalit Jain, Zohar Karnin, Kevin G. Jamieson
An Empirical Process Approach to the Union Bound: Practical Algorithms for Combinatorial and Linear Bandits. CoRR (2020)
Julian Katz-Samuels, Lalit Jain, Zohar Karnin, Kevin G. Jamieson
An Empirical Process Approach to the Union Bound: Practical Algorithms for Combinatorial and Linear Bandits. NeurIPS (2020)
Liam Li, Kevin G. Jamieson, Afshin Rostamizadeh, Ekaterina Gonina, Jonathan Ben-tzur, Moritz Hardt, Benjamin Recht, Ameet Talwalkar
A System for Massively Parallel Hyperparameter Tuning. MLSys (2020)
Lalit Jain, Kevin G. Jamieson
A New Perspective on Pool-Based Active Classification and False-Discovery Control. NeurIPS (2019)
Tanner Fiez, Lalit Jain, Kevin G. Jamieson, Lillian J. Ratliff
Sequential Experimental Design for Transductive Linear Bandits. CoRR (2019)
Liam Li, Evan R. Sparks, Kevin G. Jamieson, Ameet Talwalkar
Exploiting Reuse in Pipeline-Aware Hyperparameter Tuning. CoRR (2019)
Max Simchowitz, Kevin G. Jamieson
Non-Asymptotic Gap-Dependent Regret Bounds for Tabular MDPs. CoRR (2019)
Tanner Fiez, Lalit Jain, Kevin G. Jamieson, Lillian J. Ratliff
Sequential Experimental Design for Transductive Linear Bandits. NeurIPS (2019)
Max Simchowitz, Kevin G. Jamieson
Non-Asymptotic Gap-Dependent Regret Bounds for Tabular MDPs. NeurIPS (2019)
Laurel J. Orr, Samuel K. Ainsworth, Walter Cai, Kevin G. Jamieson, Magda Balazinska, Dan Suciu
Mosaic: A Sample-Based Database System for Open World Query Processing. CoRR (2019)
Alexander Ratner, Dan Alistarh, Gustavo Alonso, David G. Andersen, Peter Bailis, Sarah Bird, Nicholas Carlini, Bryan Catanzaro, Eric Chung, Bill Dally, Jeff Dean, Inderjit S. Dhillon, Alexandros G. Dimakis, Pradeep Dubey, Charles Elkan, Grigori Fursin, Gregory R. Ganger, Lise Getoor, Phillip B. Gibbons, Garth A. Gibson, Joseph E. Gonzalez, Justin Gottschlich, Song Han, Kim M. Hazelwood, Furong Huang, Martin Jaggi, Kevin G. Jamieson, Michael I. Jordan, Gauri Joshi, Rania Khalaf, Jason Knight, Jakub Konecný, Tim Kraska, Arun Kumar, Anastasios Kyrillidis, Jing Li, Samuel Madden, H. Brendan McMahan, Erik Meijer, Ioannis Mitliagkas, Rajat Monga, Derek Gordon Murray, Dimitris S. Papailiopoulos, Gennady Pekhimenko, Theodoros Rekatsinas, Afshin Rostamizadeh, Christopher Ré, Christopher De Sa, Hanie Sedghi, Siddhartha Sen, Virginia Smith, Alex Smola, Dawn Song, Evan R. Sparks, Ion Stoica, Vivienne Sze, Madeleine Udell, Joaquin Vanschoren, Shivaram Venkataraman, Rashmi Vinayak, Markus Weimer, Andrew Gordon Wilson, Eric P. Xing, Matei Zaharia, Ce Zhang, Ameet Talwalkar
SysML: The New Frontier of Machine Learning Systems. CoRR (2019)
Julian Katz-Samuels, Kevin G. Jamieson
The True Sample Complexity of Identifying Good Arms. CoRR (2019)
Liam Li, Kevin G. Jamieson, Afshin Rostamizadeh, Ekaterina Gonina, Moritz Hardt, Benjamin Recht, Ameet Talwalkar
Massively Parallel Hyperparameter Tuning. CoRR (2018)
Kevin G. Jamieson, Lalit Jain
A Bandit Approach to Sequential Experimental Design with False Discovery Control. NeurIPS (2018)
Max Simchowitz, Kevin G. Jamieson, Jordan W. Suchow, Thomas L. Griffiths
Adaptive Sampling for Convex Regression. CoRR (2018)
Kevin G. Jamieson, Lalit Jain
A Bandit Approach to Multiple Testing with False Discovery Control. CoRR (2018)
Lalit Jain, Kevin G. Jamieson
Firing Bandits: Optimizing Crowdfunding. ICML (2018)
Fanny Yang, Aaditya Ramdas, Kevin G. Jamieson, Martin J. Wainwright
A framework for Multi-A(rmed)/B(andit) testing with online FDR control. CoRR (2017)
Lisha Li, Kevin G. Jamieson, Giulia DeSalvo, Afshin Rostamizadeh, Ameet Talwalkar
Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization. J. Mach. Learn. Res. 18 (2017)
Michael Laskey, Caleb Chuck, Jonathan Lee, Jeffrey Mahler, Sanjay Krishnan, Kevin G. Jamieson, Anca D. Dragan, Ken Goldberg
Comparing human-centric and robot-centric sampling for robot deep learning from demonstrations. ICRA (2017)
Max Simchowitz, Kevin G. Jamieson, Benjamin Recht
The Simulator: Understanding Adaptive Sampling in the Moderate-Confidence Regime. COLT (2017)
Lisha Li, Kevin G. Jamieson, Giulia DeSalvo, Afshin Rostamizadeh, Ameet Talwalkar
Hyperband: Bandit-Based Configuration Evaluation for Hyperparameter Optimization. ICLR (Poster) (2017)
Max Simchowitz, Kevin G. Jamieson, Benjamin Recht
The Simulator: Understanding Adaptive Sampling in the Moderate-Confidence Regime. CoRR (2017)
Fanny Yang, Aaditya Ramdas, Kevin G. Jamieson, Martin J. Wainwright
A framework for Multi-A(rmed)/B(andit) Testing with Online FDR Control. NIPS (2017)
Michael Laskey, Caleb Chuck, Jonathan Lee, Jeffrey Mahler, Sanjay Krishnan, Kevin G. Jamieson, Anca D. Dragan, Kenneth Y. Goldberg
Comparing Human-Centric and Robot-Centric Sampling for Robot Deep Learning from Demonstrations. CoRR (2016)
Kevin G. Jamieson, Daniel Haas, Benjamin Recht
On the Detection of Mixture Distributions with applications to the Most Biased Coin Problem. CoRR (2016)
Lalit Jain, Kevin G. Jamieson, Robert D. Nowak
Finite Sample Prediction and Recovery Bounds for Ordinal Embedding. CoRR (2016)
Lalit Jain, Kevin G. Jamieson, Robert D. Nowak
Finite Sample Prediction and Recovery Bounds for Ordinal Embedding. NIPS (2016)
Kwang-Sung Jun, Kevin G. Jamieson, Robert D. Nowak, Xiaojin Zhu
Top Arm Identification in Multi-Armed Bandits with Batch Arm Pulls. AISTATS (2016)
Kevin G. Jamieson, Ameet Talwalkar
Non-stochastic Best Arm Identification and Hyperparameter Optimization. AISTATS (2016)
Kevin G. Jamieson, Daniel Haas, Benjamin Recht
The Power of Adaptivity in Identifying Statistical Alternatives. NIPS (2016)
Max Simchowitz, Kevin G. Jamieson, Benjamin Recht
Best-of-K Bandits. CoRR (2016)
Max Simchowitz, Kevin G. Jamieson, Benjamin Recht
Best-of-K-bandits. COLT (2016)
Lisha Li, Kevin G. Jamieson, Giulia DeSalvo, Afshin Rostamizadeh, Ameet Talwalkar
Efficient Hyperparameter Optimization and Infinitely Many Armed Bandits. CoRR (2016)
Kevin G. Jamieson, Ameet Talwalkar
Non-stochastic Best Arm Identification and Hyperparameter Optimization. CoRR (2015)
Kevin G. Jamieson, Lalit Jain, Chris Fernandez, Nicholas J. Glattard, Robert D. Nowak
NEXT: A System for Real-World Development, Evaluation, and Application of Active Learning. NIPS (2015)
Kevin G. Jamieson, Sumeet Katariya, Atul Deshpande, Robert D. Nowak
Sparse Dueling Bandits. AISTATS (2015)
Kevin G. Jamieson, Sumeet Katariya, Atul Deshpande, Robert D. Nowak
Sparse Dueling Bandits. CoRR (2015)
Kevin G. Jamieson, Robert D. Nowak
Best-arm identification algorithms for multi-armed bandits in the fixed confidence setting. CISS (2014)
Kevin G. Jamieson, Matthew Malloy, Robert D. Nowak, Sébastien Bubeck
lil' UCB : An Optimal Exploration Algorithm for Multi-Armed Bandits. COLT (2014)
Kevin G. Jamieson, Matthew Malloy, Robert D. Nowak, Sébastien Bubeck
On Finding the Largest Mean Among Many. CoRR (2013)
Kevin G. Jamieson, Matthew Malloy, Robert D. Nowak, Sébastien Bubeck
lil' UCB : An Optimal Exploration Algorithm for Multi-Armed Bandits. CoRR (2013)
Kevin G. Jamieson, Robert D. Nowak, Benjamin Recht
Query Complexity of Derivative-Free Optimization CoRR (2012)
Kevin G. Jamieson, Robert D. Nowak, Benjamin Recht
Query Complexity of Derivative-Free Optimization. NIPS (2012)
Hyrum S. Anderson, Maya R. Gupta, Eric Swanson, Kevin G. Jamieson
Channel-Robust Classifiers. IEEE Trans. Signal Process. 59 (4) (2011)
Kevin G. Jamieson, Robert D. Nowak
Low-dimensional embedding using adaptively selected ordinal data. Allerton (2011)
Kevin G. Jamieson, Robert D. Nowak
Active Ranking using Pairwise Comparisons CoRR (2011)
Kevin G. Jamieson, Robert D. Nowak
Active Ranking using Pairwise Comparisons. NIPS (2011)
Kevin G. Jamieson, Maya R. Gupta, Eric Swanson, Hyrum S. Anderson
Training a support vector machine to classify signals in a real environment given clean training data. ICASSP (2010)
Kevin G. Jamieson, Maya R. Gupta, David W. Krout
Sequential Bayesian estimation of the probability of detection for tracking. FUSION (2009)