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08/03/2023
Aligning Agent Policy with Externalities: Reward Design via Bilevel RL
In reinforcement learning (RL), a reward function is often assumed at th...
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07/26/2023
Online Modeling and Monitoring of Dependent Processes under Resource Constraints
Monitoring a population of dependent processes under limited resources i...
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07/26/2023
How Does Diffusion Influence Pretrained Language Models on Out-of-Distribution Data?
Transformer-based pretrained language models (PLMs) have achieved great ...
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07/24/2023
Provable Benefits of Policy Learning from Human Preferences in Contextual Bandit Problems
A crucial task in decision-making problems is reward engineering. It is ...
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06/21/2023
Provably Efficient Representation Learning with Tractable Planning in Low-Rank POMDP
In this paper, we study representation learning in partially observable ...
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06/13/2023
Unified Off-Policy Learning to Rank: a Reinforcement Learning Perspective
Off-policy Learning to Rank (LTR) aims to optimize a ranker from data co...
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05/30/2023
Adversarial Attacks on Online Learning to Rank with Stochastic Click Models
We propose the first study of adversarial attacks on online learning to ...
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02/08/2023
Machine Learning for Synthetic Data Generation: a Review
Data plays a crucial role in machine learning. However, in real-world ap...
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08/30/2022
Dynamic Global Sensitivity for Differentially Private Contextual Bandits
Bandit algorithms have become a reference solution for interactive recom...
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06/29/2022
Provably Efficient Reinforcement Learning for Online Adaptive Influence Maximization
Online influence maximization aims to maximize the influence spread of a...
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06/10/2022
Communication Efficient Distributed Learning for Kernelized Contextual Bandits
We tackle the communication efficiency challenge of learning kernelized ...
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06/05/2022
Bandit Theory and Thompson Sampling-Guided Directed Evolution for Sequence Optimization
Directed Evolution (DE), a landmark wet-lab method originated in 1960s, ...
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10/18/2021
When Are Linear Stochastic Bandits Attackable?
We study adversarial attacks on linear stochastic bandits, a sequential ...
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04/08/2021
Incentivizing Exploration in Linear Bandits under Information Gap
We study the problem of incentivizing exploration for myopic users in li...
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02/28/2021
PairRank: Online Pairwise Learning to Rank by Divide-and-Conquer
Online Learning to Rank (OL2R) eliminates the need of explicit relevance...
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07/16/2020
A Smoothed Analysis of Online Lasso for the Sparse Linear Contextual Bandit Problem
We investigate the sparse linear contextual bandit problem where the par...
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04/28/2020
Unbiased Learning to Rank: Online or Offline?
How to obtain an unbiased ranking model by learning to rank with biased ...
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11/12/2019
Incentivized Exploration for Multi-Armed Bandits under Reward Drift
We study incentivized exploration for the multi-armed bandit (MAB) probl...
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08/24/2019
Adversarial Domain Adaptation for Machine Reading Comprehension
In this paper, we focus on unsupervised domain adaptation for Machine Re...
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06/10/2019
Variance Reduction in Gradient Exploration for Online Learning to Rank
Online Learning to Rank (OL2R) algorithms learn from implicit user feedb...
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06/09/2019
Factorization Bandits for Online Influence Maximization
We study the problem of online influence maximization in social networks...
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05/18/2018