Kevin Jamieson

Improved Corruption Robust Algorithms for Episodic Reinforcement Learning

We study episodic reinforcement learning under unknown adversarial corru...
02/13/2021 ∙ by Yifang Chen, et al. ∙ 0 ∙ share

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Task-Optimal Exploration in Linear Dynamical Systems

Exploration in unknown environments is a fundamental problem in reinforc...
02/10/2021 ∙ by Andrew Wagenmaker, et al. ∙ 0 ∙ share

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

Autonomous robot-assisted feeding requires the ability to acquire a wide...
11/05/2020 ∙ by Ethan K. Gordon, et al. ∙ 7 ∙ share

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Experimental Design for Regret Minimization in Linear Bandits

In this paper we propose a novel experimental design-based algorithm to ...
11/01/2020 ∙ by Andrew Wagenmaker, et al. ∙ 0 ∙ share

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Learning to Actively Learn: A Robust Approach

This work proposes a procedure for designing algorithms for specific ada...
10/29/2020 ∙ by Jifan Zhang, et al. ∙ 0 ∙ share

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

In many scientific settings there is a need for adaptive experimental de...
08/14/2020 ∙ by Lalit Jain, et al. ∙ 0 ∙ share

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

This paper proposes near-optimal algorithms for the pure-exploration lin...
06/21/2020 ∙ by Julian Katz-Samuels, et al. ∙ 0 ∙ share

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Estimating the number and effect sizes of non-null hypotheses

We study the problem of estimating the distribution of effect sizes (the...
02/17/2020 ∙ by Jennifer Brennan, et al. ∙ 0 ∙ share

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Active Learning for Identification of Linear Dynamical Systems

We propose an algorithm to actively estimate the parameters of a linear ...
02/02/2020 ∙ by Andrew Wagenmaker, et al. ∙ 0 ∙ share

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

Data scientists have relied on samples to analyze populations of interes...
12/17/2019 ∙ by Laurel Orr, et al. ∙ 0 ∙ share

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Sequential Experimental Design for Transductive Linear Bandits

In this paper we introduce the transductive linear bandit problem: given...
06/20/2019 ∙ by Tanner Fiez, et al. ∙ 0 ∙ share

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The True Sample Complexity of Identifying Good Arms

We consider two multi-armed bandit problems with n arms: (i) given an ϵ ...
06/15/2019 ∙ by Julian Katz-Samuels, et al. ∙ 0 ∙ share

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Non-Asymptotic Gap-Dependent Regret Bounds for Tabular MDPs

This paper establishes that optimistic algorithms attain gap-dependent a...
05/09/2019 ∙ by Max Simchowitz, et al. ∙ 0 ∙ share

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SysML: The New Frontier of Machine Learning Systems

Machine learning (ML) techniques are enjoying rapidly increasing adoptio...
03/29/2019 ∙ by Alexander Ratner, et al. ∙ 0 ∙ share

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Exploiting Reuse in Pipeline-Aware Hyperparameter Tuning

Hyperparameter tuning of multi-stage pipelines introduces a significant ...
03/12/2019 ∙ by Liam Li, et al. ∙ 18 ∙ share

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

This paper considers a multi-armed bandit game where the number of arms ...
11/15/2018 ∙ by Maryam Aziz, et al. ∙ 0 ∙ share

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Massively Parallel Hyperparameter Tuning

Modern learning models are characterized by large hyperparameter spaces....
10/13/2018 ∙ by Liam Li, et al. ∙ 0 ∙ share

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A Bandit Approach to Multiple Testing with False Discovery Control

We propose an adaptive sampling approach for multiple testing which aims...
09/06/2018 ∙ by Kevin Jamieson, et al. ∙ 0 ∙ share

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Adaptive Sampling for Convex Regression

In this paper, we introduce the first principled adaptive-sampling proce...
08/14/2018 ∙ by Max Simchowitz, et al. ∙ 0 ∙ share

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

We propose an alternative framework to existing setups for controlling f...
06/16/2017 ∙ by Fanny Yang, et al. ∙ 0 ∙ share

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

We propose a novel technique for analyzing adaptive sampling called the ...
02/16/2017 ∙ by Max Simchowitz, et al. ∙ 0 ∙ share

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Finite Sample Prediction and Recovery Bounds for Ordinal Embedding

The goal of ordinal embedding is to represent items as points in a low-d...
06/22/2016 ∙ by Lalit Jain, et al. ∙ 0 ∙ share

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Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization

Performance of machine learning algorithms depends critically on identif...
03/21/2016 ∙ by Lisha Li, et al. ∙ 0 ∙ share

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Best-of-K Bandits

This paper studies the Best-of-K Bandit game: At each time the player ch...
03/09/2016 ∙ by Max Simchowitz, et al. ∙ 0 ∙ share

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Non-stochastic Best Arm Identification and Hyperparameter Optimization

Motivated by the task of hyperparameter optimization, we introduce the n...
02/27/2015 ∙ by Kevin Jamieson, et al. ∙ 0 ∙ share

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Sparse Dueling Bandits

The dueling bandit problem is a variation of the classical multi-armed b...
01/31/2015 ∙ by Kevin Jamieson, et al. ∙ 0 ∙ share

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lil' UCB : An Optimal Exploration Algorithm for Multi-Armed Bandits

The paper proposes a novel upper confidence bound (UCB) procedure for id...
12/27/2013 ∙ by Kevin Jamieson, et al. ∙ 0 ∙ share

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On Finding the Largest Mean Among Many

Sampling from distributions to find the one with the largest mean arises...
06/17/2013 ∙ by Kevin Jamieson, et al. ∙ 0 ∙ share

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Kevin Jamieson

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