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Kevin G. Jamieson
Kevin Jamieson 0001
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- affiliation: University of Washington, School of Computer Science, Seattle, WA, USA
- affiliation: University of California, Berkeley, Electrical Engineering and Computer Sciences Department, CA, USA
- affiliation: University of Wisconsin-Madison, Department of Electrical and Computer Engineering, WI, USA
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- Kevin Jamieson 0002 — Coho Data
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2020 – today
- 2025
- [j4]Maryam Aziz
, Jesse Anderton
Unbiased Identification of Broadly Appealing Content Using a Pure Exploration Infinitely Armed Bandit Strategy. Trans. Recomm. Syst. 3(1): 4:1-4:22 (2025) - 2024
- [c58]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: 6549-6560 - [c57]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: 1585-1593 - [c56]Zhaoqi Li, Kevin G. Jamieson, Lalit Jain:
Optimal Exploration is no harder than Thompson Sampling. AISTATS 2024: 1684-1692 - [c55]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: 2602-2610 - [i65]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 abs/2401.06692 (2024) - [i64]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 abs/2402.02055 (2024) - [i63]Yiping Wang, Yifang Chen, Wendan Yan, Alex Fang, Wenjing Zhou, Kevin Jamieson, Simon Shaolei Du:
CLIPLoss and Norm-Based Data Selection Methods for Multimodal Contrastive Learning. CoRR abs/2405.19547 (2024) - [i62]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) - [i61]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) - [i60]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 abs/2407.02119 (2024) - 2023
- [c54]Arnab Maiti, Kevin G. Jamieson, Lillian J. Ratliff:
Instance-dependent Sample Complexity Bounds for Zero-sum Matrix Games. AISTATS 2023: 9429-9469 - [c53]Yiping Wang, Yifang Chen, Kevin Jamieson, Simon Shaolei Du:
Improved Active Multi-Task Representation Learning via Lasso. ICML 2023: 35548-35578 - [c52]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 - [c51]Andrew Wagenmaker, Guanya Shi, Kevin G. Jamieson:
Optimal Exploration for Model-Based RL in Nonlinear Systems. NeurIPS 2023 - [c50]Shuai Li, Azarakhsh Keipour
Demonstrating Large-Scale Package Manipulation via Learned Metrics of Pick Success. Robotics: Science and Systems 2023 - [i59]Arnab Maiti, Kevin G. Jamieson, Lillian J. Ratliff:
Instance-dependent Sample Complexity Bounds for Zero-sum Matrix Games. CoRR abs/2303.10565 (2023) - [i58]Shuai Li, Azarakhsh Keipour, Kevin Jamieson, Nicolas Hudson, Charles Swan, Kostas E. Bekris:
Large-Scale Package Manipulation via Learned Metrics of Pick Success. CoRR abs/2305.10272 (2023) - [i57]Yiping Wang, Yifang Chen, Kevin G. Jamieson, Simon S. Du:
Improved Active Multi-Task Representation Learning via Lasso. CoRR abs/2306.02556 (2023) - [i56]Yifang Chen, Yingbing Huang, Simon S. Du, Kevin G. Jamieson, Guanya Shi:
Active Representation Learning for General Task Space with Applications in Robotics. CoRR abs/2306.08942 (2023) - [i55]Andrew Wagenmaker, Guanya Shi, Kevin Jamieson:
Optimal Exploration for Model-Based RL in Nonlinear Systems. CoRR abs/2306.09210 (2023) - [i54]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 abs/2306.09910 (2023) - [i53]Arnab Maiti, Kevin G. Jamieson, Lillian J. Ratliff:
Logarithmic Regret for Matrix Games against an Adversary with Noisy Bandit Feedback. CoRR abs/2306.13233 (2023) - [i52]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 abs/2307.15154 (2023) - [i51]Shuai Li, Azarakhsh Keipour, Kevin Jamieson, Nicolas Hudson, Sicong Zhao, Charles Swan, Kostas E. Bekris:
Pick Planning Strategies for Large-Scale Package Manipulation. CoRR abs/2309.13224 (2023) - [i50]Zhaoqi Li, Kevin G. Jamieson, Lalit Jain:
Optimal Exploration is no harder than Thompson Sampling. CoRR abs/2310.06069 (2023) - [i49]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 abs/2310.16236 (2023) - [i48]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 abs/2310.16252 (2023) - [i47]Artin Tajdini, Lalit Jain, Kevin Jamieson:
Minimax Optimal Submodular Optimization with Bandit Feedback. CoRR abs/2310.18465 (2023) - [i46]Romain Camilleri, Andrew Wagenmaker, Jamie Morgenstern, Lalit Jain, Kevin Jamieson:
Fair Active Learning in Low-Data Regimes. CoRR abs/2312.08559 (2023) - 2022
- [j3]Jennifer Brennan
Sample-efficient identification of high-dimensional antibiotic synergy with a normalized diagonal sampling design. PLoS Comput. Biol. 18(7) (2022) - [c49]Blake Mason, Lalit Jain, Subhojyoti Mukherjee
Nearly Optimal Algorithms for Level Set Estimation. AISTATS 2022: 7625-7658 - [c48]Zhenlin Wang, Andrew J. Wagenmaker, Kevin G. Jamieson:
Best Arm Identification with Safety Constraints. AISTATS 2022: 9114-9146 - [c47]Andrew J. Wagenmaker, Max Simchowitz, Kevin Jamieson:
Beyond No Regret: Instance-Dependent PAC Reinforcement Learning. COLT 2022: 358-418 - [c46]Yifang Chen, Kevin G. Jamieson, Simon S. Du:
Active Multi-Task Representation Learning. ICML 2022: 3271-3298 - [c45]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 - [c44]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 - [c43]Romain Camilleri, Andrew Wagenmaker, Jamie H. Morgenstern, Lalit Jain, Kevin G. Jamieson:
Active Learning with Safety Constraints. NeurIPS 2022 - [c42]Zhaoqi Li, Lillian J. Ratliff, Houssam Nassif, Kevin G. Jamieson, Lalit Jain:
Instance-optimal PAC Algorithms for Contextual Bandits. NeurIPS 2022 - [c41]Andrew Wagenmaker, Kevin G. Jamieson:
Instance-Dependent Near-Optimal Policy Identification in Linear MDPs via Online Experiment Design. NeurIPS 2022 - [c40]Maryam Aziz, Jesse Anderton
Identifying New Podcasts with High General Appeal Using a Pure Exploration Infinitely-Armed Bandit Strategy. RecSys 2022: 134-144 - [i45]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) - [i44]Yifang Chen, Simon S. Du, Kevin Jamieson:
Active Multi-Task Representation Learning. CoRR abs/2202.00911 (2022) - [i43]Romain Camilleri, Andrew Wagenmaker, Jamie Morgenstern, Lalit Jain, Kevin G. Jamieson:
Active Learning with Safety Constraints. CoRR abs/2206.11183 (2022) - [i42]Zhaoqi Li, Lillian J. Ratliff, Houssam Nassif, Kevin G. Jamieson, Lalit Jain:
Instance-optimal PAC Algorithms for Contextual Bandits. CoRR abs/2207.02357 (2022) - [i41]Andrew Wagenmaker, Kevin Jamieson:
Instance-Dependent Near-Optimal Policy Identification in Linear MDPs via Online Experiment Design. CoRR abs/2207.02575 (2022) - 2021
- [c39]Andrew Wagenmaker, Julian Katz-Samuels, Kevin G. Jamieson:
Experimental Design for Regret Minimization in Linear Bandits. AISTATS 2021: 3088-3096 - [c38]Romain Camilleri, Kevin Jamieson, Julian Katz-Samuels:
High-dimensional Experimental Design and Kernel Bandits. ICML 2021: 1227-1237 - [c37]Yifang Chen, Simon S. Du, Kevin Jamieson:
Improved Corruption Robust Algorithms for Episodic Reinforcement Learning. ICML 2021: 1561-1570 - [c36]Julian Katz-Samuels, Jifan Zhang, Lalit Jain, Kevin Jamieson:
Improved Algorithms for Agnostic Pool-based Active Classification. ICML 2021: 5334-5344 - [c35]Andrew J. Wagenmaker, Max Simchowitz, Kevin G. Jamieson:
Task-Optimal Exploration in Linear Dynamical Systems. ICML 2021: 10641-10652 - [c34]Ethan K. Gordon
Leveraging Post Hoc Context for Faster Learning in Bandit Settings with Applications in Robot-Assisted Feeding. ICRA 2021: 10528-10535 - [c33]Romain Camilleri, Zhihan Xiong, Maryam Fazel, Lalit Jain, Kevin G. Jamieson:
Selective Sampling for Online Best-arm Identification. NeurIPS 2021: 11071-11082 - [c32]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: 17801-17812 - [c31]Yifang Chen, Simon S. Du, Kevin G. Jamieson:
Corruption Robust Active Learning. NeurIPS 2021: 29643-29654 - [i40]Andrew Wagenmaker, Max Simchowitz, Kevin G. Jamieson:
Task-Optimal Exploration in Linear Dynamical Systems. CoRR abs/2102.05214 (2021) - [i39]Yifang Chen, Simon S. Du, Kevin Jamieson:
Improved Corruption Robust Algorithms for Episodic Reinforcement Learning. CoRR abs/2102.06875 (2021) - [i38]Romain Camilleri, Julian Katz-Samuels, Kevin G. Jamieson:
High-Dimensional Experimental Design and Kernel Bandits. CoRR abs/2105.05806 (2021) - [i37]Julian Katz-Samuels, Jifan Zhang, Lalit Jain, Kevin Jamieson:
Improved Algorithms for Agnostic Pool-based Active Classification. CoRR abs/2105.06499 (2021) - [i36]Yifang Chen, Simon S. Du, Kevin Jamieson:
Corruption Robust Active Learning. CoRR abs/2106.11220 (2021) - [i35]Andrew Wagenmaker, Max Simchowitz, Kevin G. Jamieson:
Beyond No Regret: Instance-Dependent PAC Reinforcement Learning. CoRR abs/2108.02717 (2021) - [i34]Romain Camilleri, Zhihan Xiong, Maryam Fazel, Lalit Jain, Kevin Jamieson:
Selective Sampling for Online Best-arm Identification. CoRR abs/2110.14864 (2021) - [i33]Blake Mason, Romain Camilleri, Subhojyoti Mukherjee, Kevin Jamieson, Robert D. Nowak, Lalit Jain:
Nearly Optimal Algorithms for Level Set Estimation. CoRR abs/2111.01768 (2021) - [i32]Julian Katz-Samuels, Blake Mason, Kevin Jamieson, Robert D. Nowak:
Practical, Provably-Correct Interactive Learning in the Realizable Setting: The Power of True Believers. CoRR abs/2111.04915 (2021) - [i31]Zhenlin Wang, Andrew Wagenmaker, Kevin G. Jamieson:
Best Arm Identification with Safety Constraints. CoRR abs/2111.12151 (2021) - [i30]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
- [c30]Julian Katz-Samuels, Kevin G. Jamieson:
The True Sample Complexity of Identifying Good Arms. AISTATS 2020: 1781-1791 - [c29]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 - [c28]Andrew Wagenmaker, Kevin G. Jamieson:
Active Learning for Identification of Linear Dynamical Systems. COLT 2020: 3487-3582 - [c27]Jennifer Brennan, Ramya Korlakai Vinayak, Kevin Jamieson:
Estimating the Number and Effect Sizes of Non-null Hypotheses. ICML 2020: 1123-1133 - [c26]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 - [c25]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 - [i29]Andrew Wagenmaker, Kevin G. Jamieson:
Active Learning for Identification of Linear Dynamical Systems. CoRR abs/2002.00495 (2020) - [i28]Jennifer Brennan, Ramya Korlakai Vinayak, Kevin Jamieson:
Estimating the number and effect sizes of non-null hypotheses. CoRR abs/2002.07297 (2020) - [i27]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 abs/2006.11685 (2020) - [i26]Lalit Jain, Kevin G. Jamieson:
A New Perspective on Pool-Based Active Classification and False-Discovery Control. CoRR abs/2008.06555 (2020) - [i25]Jifan Zhang, Kevin Jamieson:
Learning to Actively Learn: A Robust Approach. CoRR abs/2010.15382 (2020) - [i24]Andrew Wagenmaker, Julian Katz-Samuels, Kevin G. Jamieson:
Experimental Design for Regret Minimization in Linear Bandits. CoRR abs/2011.00576 (2020) - [i23]Ethan K. Gordon, Sumegh Roychowdhury, Tapomayukh Bhattacharjee, Kevin Jamieson, Siddhartha S. Srinivasa:
Leveraging Post Hoc Context for Faster Learning in Bandit Settings with Applications in Robot-Assisted Feeding. CoRR abs/2011.02604 (2020)
2010 – 2019
- 2019
- [c24]Max Simchowitz, Kevin G. Jamieson:
Non-Asymptotic Gap-Dependent Regret Bounds for Tabular MDPs. NeurIPS 2019: 1151-1160 - [c23]Tanner Fiez, Lalit Jain, Kevin G. Jamieson, Lillian J. Ratliff:
Sequential Experimental Design for Transductive Linear Bandits. NeurIPS 2019: 10666-10676 - [c22]Lalit Jain, Kevin G. Jamieson:
A New Perspective on Pool-Based Active Classification and False-Discovery Control. NeurIPS 2019: 13992-14003 - [i22]Liam Li, Evan Randall Sparks, Kevin G. Jamieson, Ameet Talwalkar:
Exploiting Reuse in Pipeline-Aware Hyperparameter Tuning. CoRR abs/1903.05176 (2019) - [i21]Alexander Ratner, Dan Alistarh, Gustavo Alonso, David G. Andersen, Peter Bailis, Sarah Bird, Nicholas Carlini, Bryan Catanzaro, Eric S. 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
SysML: The New Frontier of Machine Learning Systems. CoRR abs/1904.03257 (2019) - [i20]Max Simchowitz, Kevin G. Jamieson:
Non-Asymptotic Gap-Dependent Regret Bounds for Tabular MDPs. CoRR abs/1905.03814 (2019) - [i19]Julian Katz-Samuels, Kevin G. Jamieson:
The True Sample Complexity of Identifying Good Arms. CoRR abs/1906.06594 (2019) - [i18]Tanner Fiez, Lalit Jain, Kevin G. Jamieson, Lillian J. Ratliff:
Sequential Experimental Design for Transductive Linear Bandits. CoRR abs/1906.08399 (2019) - [i17]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 abs/1912.07777 (2019) - 2018
- [c21]Lalit Jain, Kevin G. Jamieson:
Firing Bandits: Optimizing Crowdfunding. ICML 2018: 2211-2219 - [c20]Kevin G. Jamieson, Lalit Jain:
A Bandit Approach to Sequential Experimental Design with False Discovery Control. NeurIPS 2018: 3664-3674 - [i16]Max Simchowitz, Kevin G. Jamieson, Jordan W. Suchow, Thomas L. Griffiths:
Adaptive Sampling for Convex Regression. CoRR abs/1808.04523 (2018) - [i15]Kevin G. Jamieson, Lalit Jain:
A Bandit Approach to Multiple Testing with False Discovery Control. CoRR abs/1809.02235 (2018) - [i14]Liam Li, Kevin G. Jamieson, Afshin Rostamizadeh, Ekaterina Gonina, Moritz Hardt, Benjamin Recht, Ameet Talwalkar:
Massively Parallel Hyperparameter Tuning. CoRR abs/1810.05934 (2018) - 2017
- [j2]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: 185:1-185:52 (2017) - [c19]Max Simchowitz, Kevin G. Jamieson, Benjamin Recht:
The Simulator: Understanding Adaptive Sampling in the Moderate-Confidence Regime. COLT 2017: 1794-1834 - [c18]Lisha Li, Kevin G. Jamieson, Giulia DeSalvo, Afshin Rostamizadeh, Ameet Talwalkar:
Hyperband: Bandit-Based Configuration Evaluation for Hyperparameter Optimization. ICLR (Poster) 2017 - [c17]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: 358-365 - [c16]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: 5957-5966 - [c15]Scott Sievert, Daniel Ross, Lalit Jain, Kevin Jamieson, Robert Nowak, Robert Mankoff:
NEXT: A system to easily connect crowdsourcing and adaptive data collection. SciPy 2017: 113-119 - [i13]Max Simchowitz, Kevin G. Jamieson, Benjamin Recht:
The Simulator: Understanding Adaptive Sampling in the Moderate-Confidence Regime. CoRR abs/1702.05186 (2017) - [i12]Fanny Yang, Aaditya Ramdas, Kevin G. Jamieson, Martin J. Wainwright:
A framework for Multi-A(rmed)/B(andit) testing with online FDR control. CoRR abs/1706.05378 (2017) - 2016
- [c14]Kwang-Sung Jun, Kevin G. Jamieson, Robert D. Nowak, Xiaojin Zhu:
Top Arm Identification in Multi-Armed Bandits with Batch Arm Pulls. AISTATS 2016: 139-148 - [c13]Kevin G. Jamieson, Ameet Talwalkar:
Non-stochastic Best Arm Identification and Hyperparameter Optimization. AISTATS 2016: 240-248 - [c12]Max Simchowitz, Kevin G. Jamieson, Benjamin Recht:
Best-of-K-bandits. COLT 2016: 1440-1489 - [c11]Kevin G. Jamieson, Daniel Haas, Benjamin Recht:
The Power of Adaptivity in Identifying Statistical Alternatives. NIPS 2016: 775-783 - [c10]Lalit Jain, Kevin G. Jamieson, Robert D. Nowak:
Finite Sample Prediction and Recovery Bounds for Ordinal Embedding. NIPS 2016: 2703-2711 - [i11]Max Simchowitz, Kevin G. Jamieson, Benjamin Recht:
Best-of-K Bandits. CoRR abs/1603.02752 (2016) - [i10]Lisha Li, Kevin G. Jamieson, Giulia DeSalvo, Afshin Rostamizadeh, Ameet Talwalkar:
Efficient Hyperparameter Optimization and Infinitely Many Armed Bandits. CoRR abs/1603.06560 (2016) - [i9]Kevin G. Jamieson, Daniel Haas, Benjamin Recht:
On the Detection of Mixture Distributions with applications to the Most Biased Coin Problem. CoRR abs/1603.08037 (2016) - [i8]Lalit Jain, Kevin G. Jamieson, Robert D. Nowak:
Finite Sample Prediction and Recovery Bounds for Ordinal Embedding. CoRR abs/1606.07081 (2016) - [i7]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 abs/1610.00850 (2016) - 2015
- [c9]Kevin G. Jamieson, Sumeet Katariya, Atul Deshpande, Robert D. Nowak:
Sparse Dueling Bandits. AISTATS 2015 - [c8]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: 2656-2664 - [i6]Kevin G. Jamieson, Sumeet Katariya, Atul Deshpande, Robert D. Nowak:
Sparse Dueling Bandits. CoRR abs/1502.00133 (2015) - [i5]Kevin G. Jamieson, Ameet Talwalkar:
Non-stochastic Best Arm Identification and Hyperparameter Optimization. CoRR abs/1502.07943 (2015) - 2014
- [c7]Kevin G. Jamieson, Robert D. Nowak:
Best-arm identification algorithms for multi-armed bandits in the fixed confidence setting. CISS 2014: 1-6 - [c6]Kevin G. Jamieson, Matthew Malloy, Robert D. Nowak, Sébastien Bubeck:
lil' UCB : An Optimal Exploration Algorithm for Multi-Armed Bandits. COLT 2014: 423-439 - 2013
- [i4]Kevin G. Jamieson, Matthew Malloy, Robert D. Nowak, Sébastien Bubeck:
On Finding the Largest Mean Among Many. CoRR abs/1306.3917 (2013) - [i3]Kevin G. Jamieson, Matthew Malloy, Robert D. Nowak, Sébastien Bubeck:
lil' UCB : An Optimal Exploration Algorithm for Multi-Armed Bandits. CoRR abs/1312.7308 (2013) - 2012
- [c5]Kevin G. Jamieson, Robert D. Nowak, Benjamin Recht:
Query Complexity of Derivative-Free Optimization. NIPS 2012: 2681-2689 - [i2]Kevin G. Jamieson, Robert D. Nowak, Benjamin Recht:
Query Complexity of Derivative-Free Optimization. CoRR abs/1209.2434 (2012) - 2011
- [j1]Hyrum S. Anderson, Maya R. Gupta, Eric Swanson, Kevin G. Jamieson:
Channel-Robust Classifiers. IEEE Trans. Signal Process. 59(4): 1421-1434 (2011) - [c4]Kevin G. Jamieson, Robert D. Nowak:
Low-dimensional embedding using adaptively selected ordinal data. Allerton 2011: 1077-1084 - [c3]Kevin G. Jamieson, Robert D. Nowak:
Active Ranking using Pairwise Comparisons. NIPS 2011: 2240-2248 - [i1]Kevin G. Jamieson, Robert D. Nowak:
Active Ranking using Pairwise Comparisons. CoRR abs/1109.3701 (2011) - 2010
- [c2]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: 2214-2217
2000 – 2009
- 2009
- [c1]Kevin G. Jamieson, Maya R. Gupta, David W. Krout:
Sequential Bayesian estimation of the probability of detection for tracking. FUSION 2009: 641-648
Coauthor Index
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last updated on 2024-10-04 20:03 CEST by the dblp team
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