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06/15/2023
Logarithmic Bayes Regret Bounds
We derive the first finite-time logarithmic regret bounds for Bayesian b...
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06/13/2023
Fixed-Budget Best-Arm Identification with Heterogeneous Reward Variances
We study the problem of best-arm identification (BAI) in the fixed-budge...
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02/03/2023
Multiplier Bootstrap-based Exploration
Despite the great interest in the bandit problem, designing efficient al...
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02/01/2023
Selective Uncertainty Propagation in Offline RL
We study the finite-horizon offline reinforcement learning (RL) problem....
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01/12/2023
Thompson Sampling with Diffusion Generative Prior
In this work, we initiate the idea of using denoising diffusion models t...
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12/09/2022
Multi-Task Off-Policy Learning from Bandit Feedback
Many practical applications, such as recommender systems and learning to...
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11/15/2022
Bayesian Fixed-Budget Best-Arm Identification
Fixed-budget best-arm identification (BAI) is a bandit problem where the...
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09/27/2022
From Ranked Lists to Carousels: A Carousel Click Model
Carousel-based recommendation interfaces allow users to explore recommen...
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06/08/2022
Uplifting Bandits
We introduce a multi-armed bandit model where the reward is a sum of mul...
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06/06/2022
Pessimistic Off-Policy Optimization for Learning to Rank
Off-policy learning is a framework for optimizing policies without deplo...
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05/30/2022
Generalizing Hierarchical Bayesian Bandits
A contextual bandit is a popular and practical framework for online lear...
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02/26/2022
Safe Exploration for Efficient Policy Evaluation and Comparison
High-quality data plays a central role in ensuring the accuracy of polic...
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02/25/2022
Meta-Learning for Simple Regret Minimization
We develop a meta-learning framework for simple regret minimization in b...
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02/03/2022
Deep Hierarchy in Bandits
Mean rewards of actions are often correlated. The form of these correlat...
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01/24/2022
IMO^3: Interactive Multi-Objective Off-Policy Optimization
Most real-world optimization problems have multiple objectives. A system...
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11/12/2021
Hierarchical Bayesian Bandits
Meta-, multi-task, and federated learning can be all viewed as solving s...
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11/08/2021
Safe Optimal Design with Applications in Policy Learning
Motivated by practical needs in online experimentation and off-policy le...
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09/16/2021
Optimal Probing with Statistical Guarantees for Network Monitoring at Scale
Cloud networks are difficult to monitor because they grow rapidly and th...
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07/13/2021
No Regrets for Learning the Prior in Bandits
We propose AdaTS, a Thompson sampling algorithm that adapts sequentially...
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06/10/2021
Thompson Sampling with a Mixture Prior
We study Thompson sampling (TS) in online decision-making problems where...
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06/09/2021
Fixed-Budget Best-Arm Identification in Contextual Bandits: A Static-Adaptive Algorithm
We study the problem of best-arm identification (BAI) in contextual band...
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03/07/2021
CORe: Capitalizing On Rewards in Bandit Exploration
We propose a bandit algorithm that explores purely by randomizing its pa...
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02/11/2021
Meta-Thompson Sampling
Efficient exploration in multi-armed bandits is a fundamental online lea...
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12/01/2020
Non-Stationary Latent Bandits
Users of recommender systems often behave in a non-stationary fashion, d...
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07/09/2020
Influence Diagram Bandits: Variational Thompson Sampling for Structured Bandit Problems
We propose a novel framework for structured bandits, which we call an in...
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06/15/2020
Latent Bandits Revisited
A latent bandit problem is one in which the learning agent knows the arm...
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06/15/2020
Piecewise-Stationary Off-Policy Optimization
Off-policy learning is a framework for evaluating and optimizing policie...
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06/09/2020
Differentiable Meta-Learning in Contextual Bandits
We study a contextual bandit setting where the learning agent has access...
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06/04/2020
Sample Efficient Graph-Based Optimization with Noisy Observations
We study sample complexity of optimizing "hill-climbing friendly" functi...
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02/17/2020
Differentiable Bandit Exploration
We learn bandit policies that maximize the average reward over bandit in...
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10/11/2019
Old Dog Learns New Tricks: Randomized UCB for Bandit Problems
We propose RandUCB, a bandit strategy that uses theoretically derived co...
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06/21/2019
Randomized Exploration in Generalized Linear Bandits
We study two randomized algorithms for generalized linear bandits, GLM-T...
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04/20/2019
Waterfall Bandits: Learning to Sell Ads Online
A popular approach to selling online advertising is by a waterfall, wher...
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04/04/2019
Empirical Bayes Regret Minimization
The prevalent approach to bandit algorithm design is to have a low-regre...
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03/21/2019
Perturbed-History Exploration in Stochastic Linear Bandits
We propose a new online algorithm for minimizing the cumulative regret i...
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02/26/2019
Perturbed-History Exploration in Stochastic Multi-Armed Bandits
We propose an online algorithm for cumulative regret minimization in a s...
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11/13/2018
Garbage In, Reward Out: Bootstrapping Exploration in Multi-Armed Bandits
We propose a multi-armed bandit algorithm that explores based on randomi...
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11/01/2018
Online Diverse Learning to Rank from Partial-Click Feedback
Learning to rank is an important problem in machine learning and recomme...
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06/15/2018
BubbleRank: Safe Online Learning to Rerank
We study the problem of online learning to re-rank, where users provide ...
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06/06/2018
TopRank: A practical algorithm for online stochastic ranking
Online learning to rank is a sequential decision-making problem where in...
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06/03/2018
Conservative Exploration using Interleaving
In many practical problems, a learning agent may want to learn the best ...
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05/24/2018
New Insights into Bootstrapping for Bandits
We investigate the use of bootstrapping in the bandit setting. We first ...
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04/27/2018
Offline Evaluation of Ranking Policies with Click Models
Many web systems rank and present a list of items to users, from recomme...
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02/11/2018
Nearly Optimal Adaptive Procedure for Piecewise-Stationary Bandit: a Change-Point Detection Approach
Multi-armed bandit (MAB) is a class of online learning problems where a ...
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12/13/2017
Stochastic Low-Rank Bandits
Many problems in computer vision and recommender systems involve low-ran...
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09/21/2017
SpectralFPL: Online Spectral Learning for Single Topic Models
This paper studies how to efficiently learn an optimal latent variable m...
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03/19/2017
Bernoulli Rank-1 Bandits for Click Feedback
The probability that a user will click a search result depends both on i...
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03/07/2017
Online Learning to Rank in Stochastic Click Models
Online learning to rank is a core problem in information retrieval and m...
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08/10/2016
Stochastic Rank-1 Bandits
We propose stochastic rank-1 bandits, a class of online learning problem...
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05/21/2016
