25 citations · 177 across the 40 of their papers we have counts for
5 papers · 1 filter
Bandit algorithms to emulate human decision making using probabilistic distortions
Ravi Kumar Kolla, Prashanth L. A., Aditya Gopalan +3
Motivated by models of human decision making proposed to explain commonly observed deviations from conventional expected value preferences, we formulate two stochastic multi-armed…
Low-rank Bandits with Latent Mixtures
Aditya Gopalan, Odalric-Ambrym Maillard, Mohammadi Zaki
We study the task of maximizing rewards from recommending items (actions) to users sequentially interacting with a recommender system. Users are modeled as latent mixtures of C man…
Optimal Recommendation to Users that React: Online Learning for a Class of POMDPs
Rahul Meshram, Aditya Gopalan, D. Manjunath
We describe and study a model for an Automated Online Recommendation System (AORS) in which a user's preferences can be time-dependent and can also depend on the history of past re…
On the Whittle Index for Restless Multi-armed Hidden Markov Bandits
Rahul Meshram, D. Manjunath, Aditya Gopalan
We consider a restless multi-armed bandit in which each arm can be in one of two states. When an arm is sampled, the state of the arm is not available to the sampler. Instead, a bi…
Collaborative Learning of Stochastic Bandits over a Social Network
Ravi Kumar Kolla, Krishna Jagannathan, Aditya Gopalan
We consider a collaborative online learning paradigm, wherein a group of agents connected through a social network are engaged in playing a stochastic multi-armed bandit game. Each…