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Improved Corruption Robust Algorithms for Episodic Reinforcement Learning

Mar 08, 2021
Yifang Chen, Simon S. Du, Kevin Jamieson

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Task-Optimal Exploration in Linear Dynamical Systems

Feb 10, 2021
Andrew Wagenmaker, Max Simchowitz, Kevin Jamieson

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Leveraging Post Hoc Context for Faster Learning in Bandit Settings with Applications in Robot-Assisted Feeding

Nov 05, 2020
Ethan K. Gordon, Sumegh Roychowdhury, Tapomayukh Bhattacharjee, Kevin Jamieson, Siddhartha S. Srinivasa

* 6 pages + acknowledgements/references, 5 figures, under review 

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Experimental Design for Regret Minimization in Linear Bandits

Nov 01, 2020
Andrew Wagenmaker, Julian Katz-Samuels, Kevin Jamieson

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Learning to Actively Learn: A Robust Approach

Oct 29, 2020
Jifan Zhang, Kevin Jamieson

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A New Perspective on Pool-Based Active Classification and False-Discovery Control

Aug 14, 2020
Lalit Jain, Kevin Jamieson

* Published at Neurips 2019 

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An Empirical Process Approach to the Union Bound: Practical Algorithms for Combinatorial and Linear Bandits

Jun 21, 2020
Julian Katz-Samuels, Lalit Jain, Zohar Karnin, Kevin Jamieson

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Estimating the number and effect sizes of non-null hypotheses

Feb 17, 2020
Jennifer Brennan, Ramya Korlakai Vinayak, Kevin Jamieson

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Active Learning for Identification of Linear Dynamical Systems

Feb 02, 2020
Andrew Wagenmaker, Kevin Jamieson

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Mosaic: A Sample-Based Database System for Open World Query Processing

Jan 10, 2020
Laurel Orr, Samuel Ainsworth, Walter Cai, Kevin Jamieson, Magda Balazinska, Dan Suciu

* CIDR 2020 

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Sequential Experimental Design for Transductive Linear Bandits

Jun 20, 2019
Tanner Fiez, Lalit Jain, Kevin Jamieson, Lillian Ratliff

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The True Sample Complexity of Identifying Good Arms

Jun 15, 2019
Julian Katz-Samuels, Kevin Jamieson

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Non-Asymptotic Gap-Dependent Regret Bounds for Tabular MDPs

May 09, 2019
Max Simchowitz, Kevin Jamieson

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SysML: The New Frontier of Machine Learning Systems

May 01, 2019
Alexander Ratner, Dan Alistarh, Gustavo Alonso, David G. Andersen, Peter Bailis, Sarah Bird, Nicholas Carlini, Bryan Catanzaro, Jennifer Chayes, Eric Chung, Bill Dally, Jeff Dean, Inderjit S. Dhillon, Alexandros 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 Hazelwood, Furong Huang, Martin Jaggi, Kevin Jamieson, Michael I. Jordan, Gauri Joshi, Rania Khalaf, Jason Knight, Jakub Konečný, Tim Kraska, Arun Kumar, Anastasios Kyrillidis, Aparna Lakshmiratan, Jing Li, Samuel Madden, H. Brendan McMahan, Erik Meijer, Ioannis Mitliagkas, Rajat Monga, Derek Murray, Kunle Olukotun, Dimitris Papailiopoulos, Gennady Pekhimenko, Theodoros Rekatsinas, Afshin Rostamizadeh, Christopher Ré, Christopher De Sa, Hanie Sedghi, Siddhartha Sen, Virginia Smith, Alex Smola, Dawn Song, Evan Sparks, Ion Stoica, Vivienne Sze, Madeleine Udell, Joaquin Vanschoren, Shivaram Venkataraman, Rashmi Vinayak, Markus Weimer, Andrew Gordon Wilson, Eric Xing, Matei Zaharia, Ce Zhang, Ameet Talwalkar

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Exploiting Reuse in Pipeline-Aware Hyperparameter Tuning

Mar 12, 2019
Liam Li, Evan Sparks, Kevin Jamieson, Ameet Talwalkar

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Pure-Exploration for Infinite-Armed Bandits with General Arm Reservoirs

Nov 15, 2018
Maryam Aziz, Kevin Jamieson, Javed Aslam

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Massively Parallel Hyperparameter Tuning

Oct 17, 2018
Liam Li, Kevin Jamieson, Afshin Rostamizadeh, Ekaterina Gonina, Moritz Hardt, Benjamin Recht, Ameet Talwalkar

* Corrected typo in Algorithm 1 

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A Bandit Approach to Multiple Testing with False Discovery Control

Sep 06, 2018
Kevin Jamieson, Lalit Jain

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Adaptive Sampling for Convex Regression

Aug 26, 2018
Max Simchowitz, Kevin Jamieson, Jordan W. Suchow, Thomas L. Griffiths

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Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization

Jun 18, 2018
Lisha Li, Kevin Jamieson, Giulia DeSalvo, Afshin Rostamizadeh, Ameet Talwalkar

* Journal of Machine Learning Research 18 (2018) 1-52 
* Changes: - Updated to JMLR version 

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Open Loop Hyperparameter Optimization and Determinantal Point Processes

Feb 14, 2018
Jesse Dodge, Kevin Jamieson, Noah A. Smith

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A framework for Multi-A(rmed)/B(andit) testing with online FDR control

Nov 18, 2017
Fanny Yang, Aaditya Ramdas, Kevin Jamieson, Martin J. Wainwright

* Published as a conference paper at NIPS 2017 

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Comparing Human-Centric and Robot-Centric Sampling for Robot Deep Learning from Demonstrations

Mar 29, 2017
Michael Laskey, Caleb Chuck, Jonathan Lee, Jeffrey Mahler, Sanjay Krishnan, Kevin Jamieson, Anca Dragan, Ken Goldberg

* Submitted to International Conference on Robotics and Automation (ICRA) 2017 

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The Simulator: Understanding Adaptive Sampling in the Moderate-Confidence Regime

Feb 16, 2017
Max Simchowitz, Kevin Jamieson, Benjamin Recht

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Finite Sample Prediction and Recovery Bounds for Ordinal Embedding

Jun 22, 2016
Lalit Jain, Kevin Jamieson, Robert Nowak

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On the Detection of Mixture Distributions with applications to the Most Biased Coin Problem

Mar 25, 2016
Kevin Jamieson, Daniel Haas, Ben Recht

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