Kevin Jamieson

research

∙ 05/17/2023

Large-Scale Package Manipulation via Learned Metrics of Pick Success

Automating warehouse operations can reduce logistics overhead costs, ult...

0 Shuai Li, et al. ∙

research

∙ 03/19/2023

Instance-dependent Sample Complexity Bounds for Zero-sum Matrix Games

We study the sample complexity of identifying an approximate equilibrium...

0 Arnab Maiti, et al. ∙

research

∙ 07/06/2022

Instance-Dependent Near-Optimal Policy Identification in Linear MDPs via Online Experiment Design

While much progress has been made in understanding the minimax sample co...

0 Andrew Wagenmaker, et al. ∙

research

∙ 07/05/2022

Instance-optimal PAC Algorithms for Contextual Bandits

In the stochastic contextual bandit setting, regret-minimizing algorithm...

0 Zhaoqi Li, et al. ∙

research

∙ 06/22/2022

Active Learning with Safety Constraints

Active learning methods have shown great promise in reducing the number ...

0 Romain Camilleri, et al. ∙

research

∙ 02/02/2022

Active Multi-Task Representation Learning

To leverage the power of big data from source tasks and overcome the sca...

14 Yifang Chen, et al. ∙

research

∙ 01/26/2022

Reward-Free RL is No Harder Than Reward-Aware RL in Linear Markov Decision Processes

Reward-free reinforcement learning (RL) considers the setting where the ...

0 Andrew Wagenmaker, et al. ∙

research

∙ 12/07/2021

First-Order Regret in Reinforcement Learning with Linear Function Approximation: A Robust Estimation Approach

Obtaining first-order regret bounds – regret bounds scaling not as the w...

0 Andrew Wagenmaker, et al. ∙

research

∙ 11/23/2021

Best Arm Identification with Safety Constraints

The best arm identification problem in the multi-armed bandit setting is...

0 Zhenlin Wang, et al. ∙

research

∙ 11/09/2021

Practical, Provably-Correct Interactive Learning in the Realizable Setting: The Power of True Believers

We consider interactive learning in the realizable setting and develop a...

0 Julian Katz-Samuels, et al. ∙

research

∙ 11/02/2021

Nearly Optimal Algorithms for Level Set Estimation

The level set estimation problem seeks to find all points in a domain X ...

0 Blake Mason, et al. ∙

research

∙ 10/28/2021

Selective Sampling for Online Best-arm Identification

This work considers the problem of selective-sampling for best-arm ident...

0 Romain Camilleri, et al. ∙

research

∙ 08/05/2021

Beyond No Regret: Instance-Dependent PAC Reinforcement Learning

The theory of reinforcement learning has focused on two fundamental prob...

0 Andrew Wagenmaker, et al. ∙

research

∙ 06/21/2021

Corruption Robust Active Learning

We conduct theoretical studies on streaming-based active learning for bi...

0 Yifang Chen, et al. ∙

research

∙ 05/13/2021

Improved Algorithms for Agnostic Pool-based Active Classification

We consider active learning for binary classification in the agnostic po...

0 Julian Katz-Samuels, et al. ∙

research

∙ 05/12/2021

High-Dimensional Experimental Design and Kernel Bandits

In recent years methods from optimal linear experimental design have bee...

0 Romain Camilleri, et al. ∙

research

∙ 02/13/2021

Improved Corruption Robust Algorithms for Episodic Reinforcement Learning

We study episodic reinforcement learning under unknown adversarial corru...

0 Yifang Chen, et al. ∙

research

∙ 02/10/2021

Task-Optimal Exploration in Linear Dynamical Systems

Exploration in unknown environments is a fundamental problem in reinforc...

0 Andrew Wagenmaker, et al. ∙

research

∙ 11/05/2020

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...

7 Ethan K. Gordon, et al. ∙

research

∙ 11/01/2020

Experimental Design for Regret Minimization in Linear Bandits

In this paper we propose a novel experimental design-based algorithm to ...

0 Andrew Wagenmaker, et al. ∙

research

∙ 10/29/2020

Learning to Actively Learn: A Robust Approach

This work proposes a procedure for designing algorithms for specific ada...

0 Jifan Zhang, et al. ∙

research

∙ 08/14/2020

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

In many scientific settings there is a need for adaptive experimental de...

0 Lalit Jain, et al. ∙

research

∙ 06/21/2020

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...

0 Julian Katz-Samuels, et al. ∙

research

∙ 02/17/2020

Estimating the number and effect sizes of non-null hypotheses

We study the problem of estimating the distribution of effect sizes (the...

0 Jennifer Brennan, et al. ∙

research

∙ 02/02/2020

Active Learning for Identification of Linear Dynamical Systems

We propose an algorithm to actively estimate the parameters of a linear ...

0 Andrew Wagenmaker, et al. ∙

research

∙ 12/17/2019

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

Data scientists have relied on samples to analyze populations of interes...

0 Laurel Orr, et al. ∙

research

∙ 06/20/2019

Sequential Experimental Design for Transductive Linear Bandits

In this paper we introduce the transductive linear bandit problem: given...

0 Tanner Fiez, et al. ∙

research

∙ 06/15/2019

The True Sample Complexity of Identifying Good Arms

We consider two multi-armed bandit problems with n arms: (i) given an ϵ ...

0 Julian Katz-Samuels, et al. ∙

research

∙ 05/09/2019

Non-Asymptotic Gap-Dependent Regret Bounds for Tabular MDPs

This paper establishes that optimistic algorithms attain gap-dependent a...

0 Max Simchowitz, et al. ∙

research

∙ 03/29/2019

SysML: The New Frontier of Machine Learning Systems

Machine learning (ML) techniques are enjoying rapidly increasing adoptio...

0 Alexander Ratner, et al. ∙

research

∙ 03/12/2019

Exploiting Reuse in Pipeline-Aware Hyperparameter Tuning

Hyperparameter tuning of multi-stage pipelines introduces a significant ...

18 Liam Li, et al. ∙

research

∙ 11/15/2018

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

This paper considers a multi-armed bandit game where the number of arms ...

0 Maryam Aziz, et al. ∙

research

∙ 10/13/2018

Massively Parallel Hyperparameter Tuning

Modern learning models are characterized by large hyperparameter spaces....

0 Liam Li, et al. ∙

research

∙ 09/06/2018

A Bandit Approach to Multiple Testing with False Discovery Control

We propose an adaptive sampling approach for multiple testing which aims...

0 Kevin Jamieson, et al. ∙

research

∙ 08/14/2018

Adaptive Sampling for Convex Regression

In this paper, we introduce the first principled adaptive-sampling proce...

0 Max Simchowitz, et al. ∙

research

∙ 06/16/2017

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

We propose an alternative framework to existing setups for controlling f...

0 Fanny Yang, et al. ∙

research

∙ 02/16/2017

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

We propose a novel technique for analyzing adaptive sampling called the ...

0 Max Simchowitz, et al. ∙

research

∙ 06/22/2016

Finite Sample Prediction and Recovery Bounds for Ordinal Embedding

The goal of ordinal embedding is to represent items as points in a low-d...

0 Lalit Jain, et al. ∙

research

∙ 03/21/2016

Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization

Performance of machine learning algorithms depends critically on identif...

0 Lisha Li, et al. ∙

research

∙ 03/09/2016

Best-of-K Bandits

This paper studies the Best-of-K Bandit game: At each time the player ch...

0 Max Simchowitz, et al. ∙

research

∙ 02/27/2015

Non-stochastic Best Arm Identification and Hyperparameter Optimization

Motivated by the task of hyperparameter optimization, we introduce the n...

0 Kevin Jamieson, et al. ∙

research

∙ 01/31/2015

Sparse Dueling Bandits

The dueling bandit problem is a variation of the classical multi-armed b...

0 Kevin Jamieson, et al. ∙

research

∙ 12/27/2013

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

The paper proposes a novel upper confidence bound (UCB) procedure for id...

0 Kevin Jamieson, et al. ∙

research

∙ 06/17/2013

On Finding the Largest Mean Among Many

Sampling from distributions to find the one with the largest mean arises...

0 Kevin Jamieson, et al. ∙

Kevin Jamieson

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