4 citations · 9 across the 12 of their papers we have counts for
13 papers
Almost Asymptotically Optimal Active Clustering Through Pairwise Observations
Rachel S. Y. Teo, P. N. Karthik, Ramya Korlakai Vinayak +1
We propose a new analysis framework for clustering items into an unknown number of distinct groups using noisy and actively collected responses. At each time step, an agent…
Asymptotically Optimal Sequential Testing with Markovian Data
Alhad Sethi, Kavali Sofia Sagar, Shubhada Agrawal +2
We study one-sided and -correct sequential hypothesis testing for data generated by an ergodic, finite-state Markov chain. The null hypothesis is that the unknown transition mat…
Optimal Multi-Objective Best Arm Identification with Fixed Confidence
Zhirui Chen, P. N. Karthik, Yeow Meng Chee +1
We consider a multi-armed bandit setting with finitely many arms, in which each arm yields an -dimensional vector reward upon selection. We assume that the reward of each dimens…
Fixed-Budget Differentially Private Best Arm Identification
Zhirui Chen, P. N. Karthik, Yeow Meng Chee +1
We study best arm identification (BAI) in linear bandits in the fixed-budget regime under differential privacy constraints, when the arm rewards are supported on the unit interval.…
Optimal Best Arm Identification with Fixed Confidence in Restless Bandits
P. N. Karthik, Vincent Y. F. Tan, Arpan Mukherjee +1
We study best arm identification in a restless multi-armed bandit setting with finitely many arms. The discrete-time data generated by each arm forms a homogeneous Markov chain tak…
Best Arm Identification in Bandits with Limited Precision Sampling
Kota Srinivas Reddy, P. N. Karthik, Nikhil Karamchandani +1
We study best arm identification in a variant of the multi-armed bandit problem where the learner has limited precision in arm selection. The learner can only sample arms via certa…
Federated Best Arm Identification with Heterogeneous Clients
Zhirui Chen, P. N. Karthik, Vincent Y. F. Tan +1
We study best arm identification in a federated multi-armed bandit setting with a central server and multiple clients, when each client has access to a {\em subset} of arms and eac…
Almost Cost-Free Communication in Federated Best Arm Identification
Kota Srinivas Reddy, P. N. Karthik, Vincent Y. F. Tan
We study the problem of best arm identification in a federated learning multi-armed bandit setup with a central server and multiple clients. Each client is associated with a multi-…
Best Arm Identification in Restless Markov Multi-Armed Bandits
P. N. Karthik, Kota Srinivas Reddy, Vincent Y. F. Tan
We study the problem of identifying the best arm in a multi-armed bandit environment when each arm is a time-homogeneous and ergodic discrete-time Markov process on a common, finit…
Learning to Detect an Odd Restless Markov Arm with a Trembling Hand
P. N. Karthik, Rajesh Sundaresan
This paper studies the problem of finding an anomalous arm in a multi-armed bandit when (a) each arm is a finite-state Markov process, and (b) the arms are restless. Here, anomaly…
Detecting an Odd Restless Markov Arm with a Trembling Hand
P. N. Karthik, Rajesh Sundaresan
In this paper, we consider a multi-armed bandit in which each arm is a Markov process evolving on a finite state space. The state space is common across the arms, and the arms are…
Learning to Detect an Odd Markov Arm
P. N. Karthik, Rajesh Sundaresan
A multi-armed bandit with finitely many arms is studied when each arm is a homogeneous Markov process on an underlying finite state space. The transition law of one of the arms, re…
On The Equivalence of Projections In Relative -Entropy and Rényi Divergence
P. N. Karthik, Rajesh Sundaresan
The aim of this work is to establish that two recently published projection theorems, one dealing with a parametric generalization of relative entropy and another dealing with Rény…