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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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02/01/2023
Selective Uncertainty Propagation in Offline RL
We study the finite-horizon offline reinforcement learning (RL) problem....
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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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05/30/2022
Generalizing Hierarchical Bayesian Bandits
A contextual bandit is a popular and practical framework for online lear...
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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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02/16/2022
Task-Agnostic Graph Explanations
Graph Neural Networks (GNNs) have emerged as powerful tools to encode gr...
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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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11/08/2021
Cold Brew: Distilling Graph Node Representations with Incomplete or Missing Neighborhoods
Graph Neural Networks (GNNs) have achieved state of the art performance ...
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10/26/2021
Probabilistic Entity Representation Model for Reasoning over Knowledge Graphs
Logical reasoning over Knowledge Graphs (KGs) is a fundamental technique...
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12/23/2020
Self-Supervised Hyperboloid Representations from Logical Queries over Knowledge Graphs
Knowledge Graphs (KGs) are ubiquitous structures for information storage...
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09/21/2020
Robust Outlier Arm Identification
We study the problem of Robust Outlier Arm Identification (ROAI), where ...
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06/03/2019
MaxGap Bandit: Adaptive Algorithms for Approximate Ranking
This paper studies the problem of adaptively sampling from K distributio...
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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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02/20/2018
Adaptive Sampling for Coarse Ranking
We consider the problem of active coarse ranking, where the goal is to s...
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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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08/10/2016
Stochastic Rank-1 Bandits
We propose stochastic rank-1 bandits, a class of online learning problem...
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02/09/2016
DCM Bandits: Learning to Rank with Multiple Clicks
A search engine recommends to the user a list of web pages. The user exa...
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01/31/2015