30 citations · 99 across the 10 of their papers we have counts for
10 papers
Multi-Armed Bandits with Dependent Arms
Rahul Singh, Fang Liu, Yin Sun +1
We study a variant of the classical multi-armed bandit problem (MABP) which we call as Multi-Armed Bandits with dependent arms. More specifically, multiple arms are grouped togethe…
A Partially Observable MDP Approach for Sequential Testing for Infectious Diseases such as COVID-19
Rahul Singh, Fang Liu, Ness B. Shroff
The outbreak of the novel coronavirus (COVID-19) is unfolding as a major international crisis whose influence extends to every aspect of our daily lives. Effective testing allows i…
Contextual Bandits with Side-Observations
Rahul Singh, Fang Liu, Xin Liu +1
We investigate contextual bandits in the presence of side-observations across arms in order to design recommendation algorithms for users connected via social networks. Users in so…
Data Poisoning Attacks on Stochastic Bandits
Fang Liu, Ness Shroff
Stochastic multi-armed bandits form a class of online learning problems that have important applications in online recommendation systems, adaptive medical treatment, and many othe…
Analysis of Thompson Sampling for Graphical Bandits Without the Graphs
Fang Liu, Zizhan Zheng, Ness Shroff
We study multi-armed bandit problems with graph feedback, in which the decision maker is allowed to observe the neighboring actions of the chosen action, in a setting where the gra…
UCBoost: A Boosting Approach to Tame Complexity and Optimality for Stochastic Bandits
Fang Liu, Sinong Wang, Swapna Buccapatnam +1
In this work, we address the open problem of finding low-complexity near-optimal multi-armed bandit algorithms for sequential decision making problems. Existing bandit algorithms a…
A Change-Detection based Framework for Piecewise-stationary Multi-Armed Bandit Problem
Fang Liu, Joohyun Lee, Ness Shroff
The multi-armed bandit problem has been extensively studied under the stationary assumption. However in reality, this assumption often does not hold because the distributions of re…
Information Directed Sampling for Stochastic Bandits with Graph Feedback
Fang Liu, Swapna Buccapatnam, Ness Shroff
We consider stochastic multi-armed bandit problems with graph feedback, where the decision maker is allowed to observe the neighboring actions of the chosen action. We allow the gr…
Reward Maximization Under Uncertainty: Leveraging Side-Observations on Networks
Swapna Buccapatnam, Fang Liu, Atilla Eryilmaz +1
We study the stochastic multi-armed bandit (MAB) problem in the presence of side-observations across actions that occur as a result of an underlying network structure. In our model…
Non-additive Security Games
Sinong Wang, Fang Liu, Ness Shroff
We have investigated the security game under non-additive utility functions.