25 citations · 177 across the 40 of their papers we have counts for
5 papers · 1 filter
Learning to detect an oddball target with observations from an exponential family
Gayathri R Prabhu, Srikrishna Bhashyam, Aditya Gopalan +1
The problem of detecting an odd arm from a set of K arms of a multi-armed bandit, with fixed confidence, is studied in a sequential decision-making scenario. Each arm's signal foll…
Online Learning for Structured Loss Spaces
Siddharth Barman, Aditya Gopalan, Aadirupa Saha
We consider prediction with expert advice when the loss vectors are assumed to lie in a set described by the sum of atomic norm balls. We derive a regret bound for a general versio…
On Kernelized Multi-armed Bandits
Sayak Ray Chowdhury, Aditya Gopalan
We consider the stochastic bandit problem with a continuous set of arms, with the expected reward function over the arms assumed to be fixed but unknown. We provide two new Gaussia…
Misspecified Linear Bandits
Avishek Ghosh, Sayak Ray Chowdhury, Aditya Gopalan
We consider the problem of online learning in misspecified linear stochastic multi-armed bandit problems. Regret guarantees for state-of-the-art linear bandit algorithms such as Op…
A Hidden Markov Restless Multi-armed Bandit Model for Playout Recommendation Systems
Rahul Meshram, Aditya Gopalan, D. Manjunath
We consider a restless multi-armed bandit (RMAB) in which there are two types of arms, say A and B. Each arm can be in one of two states, say or Playing a type A arm bring…