
Improved Corruption Robust Algorithms for Episodic Reinforcement Learning
We study episodic reinforcement learning under unknown adversarial corru...
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TaskOptimal Exploration in Linear Dynamical Systems
Exploration in unknown environments is a fundamental problem in reinforc...
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Leveraging Post Hoc Context for Faster Learning in Bandit Settings with Applications in RobotAssisted Feeding
Autonomous robotassisted feeding requires the ability to acquire a wide...
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Experimental Design for Regret Minimization in Linear Bandits
In this paper we propose a novel experimental designbased algorithm to ...
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Learning to Actively Learn: A Robust Approach
This work proposes a procedure for designing algorithms for specific ada...
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A New Perspective on PoolBased Active Classification and FalseDiscovery Control
In many scientific settings there is a need for adaptive experimental de...
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An Empirical Process Approach to the Union Bound: Practical Algorithms for Combinatorial and Linear Bandits
This paper proposes nearoptimal algorithms for the pureexploration lin...
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Estimating the number and effect sizes of nonnull hypotheses
We study the problem of estimating the distribution of effect sizes (the...
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Active Learning for Identification of Linear Dynamical Systems
We propose an algorithm to actively estimate the parameters of a linear ...
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Mosaic: A SampleBased Database System for Open World Query Processing
Data scientists have relied on samples to analyze populations of interes...
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Sequential Experimental Design for Transductive Linear Bandits
In this paper we introduce the transductive linear bandit problem: given...
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The True Sample Complexity of Identifying Good Arms
We consider two multiarmed bandit problems with n arms: (i) given an ϵ ...
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NonAsymptotic GapDependent Regret Bounds for Tabular MDPs
This paper establishes that optimistic algorithms attain gapdependent a...
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SysML: The New Frontier of Machine Learning Systems
Machine learning (ML) techniques are enjoying rapidly increasing adoptio...
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Exploiting Reuse in PipelineAware Hyperparameter Tuning
Hyperparameter tuning of multistage pipelines introduces a significant ...
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PureExploration for InfiniteArmed Bandits with General Arm Reservoirs
This paper considers a multiarmed bandit game where the number of arms ...
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Massively Parallel Hyperparameter Tuning
Modern learning models are characterized by large hyperparameter spaces....
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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...
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Adaptive Sampling for Convex Regression
In this paper, we introduce the first principled adaptivesampling proce...
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A framework for MultiA(rmed)/B(andit) testing with online FDR control
We propose an alternative framework to existing setups for controlling f...
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The Simulator: Understanding Adaptive Sampling in the ModerateConfidence Regime
We propose a novel technique for analyzing adaptive sampling called the ...
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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 lowd...
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Hyperband: A Novel BanditBased Approach to Hyperparameter Optimization
Performance of machine learning algorithms depends critically on identif...
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BestofK Bandits
This paper studies the BestofK Bandit game: At each time the player ch...
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Nonstochastic Best Arm Identification and Hyperparameter Optimization
Motivated by the task of hyperparameter optimization, we introduce the n...
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Sparse Dueling Bandits
The dueling bandit problem is a variation of the classical multiarmed b...
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lil' UCB : An Optimal Exploration Algorithm for MultiArmed Bandits
The paper proposes a novel upper confidence bound (UCB) procedure for id...
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On Finding the Largest Mean Among Many
Sampling from distributions to find the one with the largest mean arises...
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Kevin Jamieson
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