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09/05/2023
Distributionally Robust Model-based Reinforcement Learning with Large State Spaces
Three major challenges in reinforcement learning are the complex dynamic...
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06/29/2023
Safe Model-Based Multi-Agent Mean-Field Reinforcement Learning
Many applications, e.g., in shared mobility, require coordinating a larg...
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02/08/2023
Efficient Planning in Combinatorial Action Spaces with Applications to Cooperative Multi-Agent Reinforcement Learning
A practical challenge in reinforcement learning are combinatorial action...
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10/14/2022
Movement Penalized Bayesian Optimization with Application to Wind Energy Systems
Contextual Bayesian optimization (CBO) is a powerful framework for seque...
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07/13/2022
Graph Neural Network Bandits
We consider the bandit optimization problem with the reward function def...
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11/09/2021
Misspecified Gaussian Process Bandit Optimization
We consider the problem of optimizing a black-box function based on nois...
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11/05/2021
Risk-averse Heteroscedastic Bayesian Optimization
Many black-box optimization tasks arising in high-stakes applications re...
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07/13/2021
Contextual Games: Multi-Agent Learning with Side Information
We formulate the novel class of contextual games, a type of repeated gam...
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07/08/2021
Efficient Model-Based Multi-Agent Mean-Field Reinforcement Learning
Learning in multi-agent systems is highly challenging due to the inheren...
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03/18/2021
Combining Pessimism with Optimism for Robust and Efficient Model-Based Deep Reinforcement Learning
In real-world tasks, reinforcement learning (RL) agents frequently encou...
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01/17/2021
Cost-Efficient Online Hyperparameter Optimization
Recent work on hyperparameters optimization (HPO) has shown the possibil...
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07/10/2020
Learning to Play Sequential Games versus Unknown Opponents
We consider a repeated sequential game between a learner, who plays firs...
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07/07/2020
Stochastic Linear Bandits Robust to Adversarial Attacks
We consider a stochastic linear bandit problem in which the rewards are ...
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03/04/2020
Corruption-Tolerant Gaussian Process Bandit Optimization
We consider the problem of optimizing an unknown (typically non-convex) ...
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02/28/2020
Mixed Strategies for Robust Optimization of Unknown Objectives
We consider robust optimization problems, where the goal is to optimize ...
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02/20/2020
Distributionally Robust Bayesian Optimization
Robustness to distributional shift is one of the key challenges of conte...
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09/18/2019
No-Regret Learning in Unknown Games with Correlated Payoffs
We consider the problem of learning to play a repeated multi-agent game ...
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10/25/2018
Adversarially Robust Optimization with Gaussian Processes
In this paper, we consider the problem of Gaussian process (GP) optimiza...
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02/20/2018
Robust Maximization of Non-Submodular Objectives
We study the problem of maximizing a monotone set function subject to a ...
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02/20/2018
High-Dimensional Bayesian Optimization via Additive Models with Overlapping Groups
Bayesian optimization (BO) is a popular technique for sequential black-b...
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11/07/2017
Streaming Robust Submodular Maximization: A Partitioned Thresholding Approach
We study the classical problem of maximizing a monotone submodular funct...
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06/15/2017
Robust Submodular Maximization: A Non-Uniform Partitioning Approach
We study the problem of maximizing a monotone submodular function subjec...
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10/24/2016
Truncated Variance Reduction: A Unified Approach to Bayesian Optimization and Level-Set Estimation
We present a new algorithm, truncated variance reduction (TruVaR), that ...
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01/25/2016
Time-Varying Gaussian Process Bandit Optimization
We consider the sequential Bayesian optimization problem with bandit fee...
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10/21/2015