Search Results for author: P. N. Karthik

Found 8 papers, 0 papers with code

Fixed-Budget Differentially Private Best Arm Identification

no code implementations17 Jan 2024 Zhirui Chen, P. N. Karthik, Yeow Meng Chee, Vincent Y. F. Tan

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

no code implementations20 Oct 2023 P. N. Karthik, Vincent Y. F. Tan, Arpan Mukherjee, Ali Tajer

It is shown that under every policy, the state-action visitation proportions satisfy a specific approximate flow conservation constraint and that these proportions match the optimal proportions dictated by the lower bound under any asymptotically optimal policy.

Best Arm Identification in Bandits with Limited Precision Sampling

no code implementations10 May 2023 Kota Srinivas Reddy, P. N. Karthik, Nikhil Karamchandani, Jayakrishnan Nair

The pulled arm and its instantaneous reward are revealed to the learner, whose goal is to find the best arm by minimising the expected stopping time, subject to an upper bound on the error probability.

Federated Best Arm Identification with Heterogeneous Clients

no code implementations14 Oct 2022 Zhirui Chen, P. N. Karthik, Vincent Y. F. Tan, Yeow Meng Chee

Furthermore, we show that for any algorithm whose upper bound on the expected stopping time matches with the lower bound up to a multiplicative constant ({\em almost-optimal} algorithm), the ratio of any two consecutive communication time instants must be {\em bounded}, a result that is of independent interest.

Almost Cost-Free Communication in Federated Best Arm Identification

no code implementations19 Aug 2022 Kota Srinivas Reddy, P. N. Karthik, Vincent Y. F. Tan

The local best arm at a client is the arm with the largest mean among the arms local to the client, whereas the global best arm is the arm with the largest average mean across all the clients.

Federated Learning

Best Arm Identification in Restless Markov Multi-Armed Bandits

no code implementations29 Mar 2022 P. N. Karthik, Kota Srinivas Reddy, Vincent Y. F. Tan

For this problem, we derive the first-known problem instance-dependent asymptotic lower bound on the growth rate of the expected time required to find the index of the best arm, where the asymptotics is as the error probability vanishes.

Multi-Armed Bandits

Learning to Detect an Odd Restless Markov Arm with a Trembling Hand

no code implementations8 May 2021 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.

Detecting an Odd Restless Markov Arm with a Trembling Hand

no code implementations13 May 2020 P. N. Karthik, Rajesh Sundaresan

The state space is common across the arms, and the arms are independent of each other.

Cannot find the paper you are looking for? You can Submit a new open access paper.