20 citations · 59 across the 4 of their papers we have counts for
4 papers
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…
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…
Mining MOOC Clickstreams: On the Relationship Between Learner Behavior and Performance
Christopher G. Brinton, Swapna Buccapatnam, Mung Chiang +1
We study student behavior and performance in two Massive Open Online Courses (MOOCs). In doing so, we present two frameworks by which video-watching clickstreams can be represented…