research
∙
08/16/2023
Robust Bayesian Satisficing
Distributional shifts pose a significant challenge to achieving robustne...
share
research
∙
06/06/2022
Robust Pareto Set Identification with Contaminated Bandit Feedback
We consider the Pareto set identification (PSI) problem in multi-objecti...
research
∙
05/09/2022
Federated Multi-Armed Bandits Under Byzantine Attacks
Multi-armed bandits (MAB) is a simple reinforcement learning model where...
research
∙
12/13/2021
Safe Linear Leveling Bandits
Multi-armed bandits (MAB) are extensively studied in various settings wh...
research
∙
11/29/2021
Contextual Combinatorial Volatile Bandits with Satisfying via Gaussian Processes
In many real-world applications of combinatorial bandits such as content...
research
∙
11/26/2021
ESCADA: Efficient Safety and Context Aware Dose Allocation for Precision Medicine
Finding an optimal individualized treatment regimen is considered one of...
research
∙
10/23/2021
Vector Optimization with Stochastic Bandit Feedback
We introduce vector optimization problems with stochastic bandit feedbac...
research
∙
10/05/2021
Contextual Combinatorial Volatile Bandits via Gaussian Processes
We consider a contextual bandit problem with a combinatorial action set ...
research
∙
09/08/2021
Conservative Policy Construction Using Variational Autoencoders for Logged Data with Missing Values
In high-stakes applications of data-driven decision making like healthca...
research
∙
06/24/2020
Pareto Active Learning with Gaussian Processes and Adaptive Discretization
We consider the problem of optimizing a vector-valued objective function...
research
∙
07/26/2019
Lexicographic Multiarmed Bandit
We consider a multiobjective multiarmed bandit problem with lexicographi...
research
∙
07/07/2019
Thompson Sampling for Combinatorial Network Optimization in Unknown Environments
Influence maximization, item recommendation, adaptive routing and dynami...
research
∙
07/01/2019
Exploiting Relevance for Online Decision-Making in High-Dimensions
Many sequential decision-making tasks require choosing at each decision ...
research
∙
05/02/2019
Long term impact of fair machine learning in sequential decision making: representation disparity and group retention
Machine learning models trained on data from multiple demographic groups...
research
∙
09/07/2018
Thompson Sampling for Combinatorial Multi-armed Bandit with Probabilistically Triggered Arms
We analyze the regret of combinatorial Thompson sampling (CTS) for the c...
research
∙
03/11/2018
Combinatorial Multi-Objective Multi-Armed Bandit Problem
In this paper, we introduce the COmbinatorial Multi-Objective Multi-Arme...
research
∙
03/11/2018
Multi-objective Contextual Bandit Problem with Similarity Information
In this paper we propose the multi-objective contextual bandit problem w...
research
∙
07/24/2017
Combinatorial Multi-armed Bandit with Probabilistically Triggered Arms: A Case with Bounded Regret
In this paper, we study the combinatorial multi-armed bandit problem (CM...
research
∙
12/23/2015
Adaptive Ensemble Learning with Confidence Bounds
Extracting actionable intelligence from distributed, heterogeneous, corr...
research
∙
08/04/2015
Staged Multi-armed Bandits
In this paper, we introduce a new class of reinforcement learning method...
research
∙
02/05/2015
RELEAF: An Algorithm for Learning and Exploiting Relevance
Recommender systems, medical diagnosis, network security, etc., require ...
research
∙
09/26/2013
Distributed Online Learning in Social Recommender Systems
In this paper, we consider decentralized sequential decision making in d...
research
∙
08/21/2013
Distributed Online Learning via Cooperative Contextual Bandits
In this paper we propose a novel framework for decentralized, online lea...
research
∙
07/02/2013
Distributed Online Big Data Classification Using Context Information
Distributed, online data mining systems have emerged as a result of appl...
research
∙
05/15/2013
