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07/27/2023
A/B Testing and Best-arm Identification for Linear Bandits with Robustness to Non-stationarity
We investigate the fixed-budget best-arm identification (BAI) problem fo...
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06/22/2023
Logarithmic Regret for Matrix Games against an Adversary with Noisy Bandit Feedback
This paper considers a variant of zero-sum matrix games where at each ti...
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06/16/2023
LabelBench: A Comprehensive Framework for Benchmarking Label-Efficient Learning
Labeled data are critical to modern machine learning applications, but o...
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06/15/2023
Optimal Exploration for Model-Based RL in Nonlinear Systems
Learning to control unknown nonlinear dynamical systems is a fundamental...
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06/15/2023
Active Representation Learning for General Task Space with Applications in Robotics
Representation learning based on multi-task pretraining has become a pow...
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06/05/2023
Improved Active Multi-Task Representation Learning via Lasso
To leverage the copious amount of data from source tasks and overcome th...
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05/17/2023
Large-Scale Package Manipulation via Learned Metrics of Pick Success
Automating warehouse operations can reduce logistics overhead costs, ult...
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03/19/2023
Instance-dependent Sample Complexity Bounds for Zero-sum Matrix Games
We study the sample complexity of identifying an approximate equilibrium...
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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...
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07/05/2022
Instance-optimal PAC Algorithms for Contextual Bandits
In the stochastic contextual bandit setting, regret-minimizing algorithm...
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06/22/2022
Active Learning with Safety Constraints
Active learning methods have shown great promise in reducing the number ...
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02/02/2022
Active Multi-Task Representation Learning
To leverage the power of big data from source tasks and overcome the sca...
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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 ...
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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...
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11/23/2021
Best Arm Identification with Safety Constraints
The best arm identification problem in the multi-armed bandit setting is...
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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...
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11/02/2021
Nearly Optimal Algorithms for Level Set Estimation
The level set estimation problem seeks to find all points in a domain X ...
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10/28/2021
Selective Sampling for Online Best-arm Identification
This work considers the problem of selective-sampling for best-arm ident...
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08/05/2021
Beyond No Regret: Instance-Dependent PAC Reinforcement Learning
The theory of reinforcement learning has focused on two fundamental prob...
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06/21/2021
Corruption Robust Active Learning
We conduct theoretical studies on streaming-based active learning for bi...
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05/13/2021
Improved Algorithms for Agnostic Pool-based Active Classification
We consider active learning for binary classification in the agnostic po...
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05/12/2021
High-Dimensional Experimental Design and Kernel Bandits
In recent years methods from optimal linear experimental design have bee...
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02/13/2021
Improved Corruption Robust Algorithms for Episodic Reinforcement Learning
We study episodic reinforcement learning under unknown adversarial corru...
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02/10/2021
Task-Optimal Exploration in Linear Dynamical Systems
Exploration in unknown environments is a fundamental problem in reinforc...
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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...
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11/01/2020
Experimental Design for Regret Minimization in Linear Bandits
In this paper we propose a novel experimental design-based algorithm to ...
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10/29/2020
Learning to Actively Learn: A Robust Approach
This work proposes a procedure for designing algorithms for specific ada...
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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...
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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...
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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...
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02/02/2020
Active Learning for Identification of Linear Dynamical Systems
We propose an algorithm to actively estimate the parameters of a linear ...
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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...
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06/20/2019
Sequential Experimental Design for Transductive Linear Bandits
In this paper we introduce the transductive linear bandit problem: given...
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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 ...
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05/09/2019
Non-Asymptotic Gap-Dependent Regret Bounds for Tabular MDPs
This paper establishes that optimistic algorithms attain gap-dependent a...
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03/29/2019
SysML: The New Frontier of Machine Learning Systems
Machine learning (ML) techniques are enjoying rapidly increasing adoptio...
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03/12/2019
Exploiting Reuse in Pipeline-Aware Hyperparameter Tuning
Hyperparameter tuning of multi-stage pipelines introduces a significant ...
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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 ...
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10/13/2018
Massively Parallel Hyperparameter Tuning
Modern learning models are characterized by large hyperparameter spaces....
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09/06/2018
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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08/14/2018
Adaptive Sampling for Convex Regression
In this paper, we introduce the first principled adaptive-sampling proce...
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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...
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02/16/2017
The Simulator: Understanding Adaptive Sampling in the Moderate-Confidence Regime
We propose a novel technique for analyzing adaptive sampling called the ...
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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...
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03/21/2016
Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization
Performance of machine learning algorithms depends critically on identif...
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03/09/2016
Best-of-K Bandits
This paper studies the Best-of-K Bandit game: At each time the player ch...
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02/27/2015
Non-stochastic Best Arm Identification and Hyperparameter Optimization
Motivated by the task of hyperparameter optimization, we introduce the n...
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01/31/2015
Sparse Dueling Bandits
The dueling bandit problem is a variation of the classical multi-armed b...
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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...
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06/17/2013