1 citations · 1 across the 6 of their papers we have counts for
7 papers
Cost-Aware Best-LLM Identification using Dueling Feedback
Sarvesh Gharat, Nikhil Karamchandani, Jayakrishnan Nair
Inspired by the problem of identifying the best model from a collection of large language models (LLMs) with heterogeneous querying costs, we formulate and analyse a variant of the…
Sequence prediction under a lying oracle
Puspabeethi Samanta, Nikhil Karamchandani, Jayakrishnan Nair
We consider the problem of sequential prediction of an -ary sequence, where at each epoch, (i) the environment selects an outcome from an -ary alphabet, (ii) the learner sele…
Small-Error Cascaded Group Testing
Daniel McMorrow, Nikhil Karamchandani, Sidharth Jaggi
Group testing concerns itself with the accurate recovery of a set of "defective" items from a larger population via a series of tests. While most works in this area have considered…
Near Optimal Best Arm Identification for Clustered Bandits
Yash, Nikhil Karamchandani, Avishek Ghosh
This work investigates the problem of best arm identification for multi-agent multi-armed bandits. We consider agents grouped into clusters, where each cluster solves a sto…
Representative Arm Identification: A fixed confidence approach to identify cluster representatives
Sarvesh Gharat, Aniket Yadav, Nikhil Karamchandani +1
We study the representative arm identification (RAI) problem in the multi-armed bandits (MAB) framework, wherein we have a collection of arms, each associated with an unknown rewar…
On Distributed Multi-User Secret Sharing with Multiple Secrets per User
Rasagna Chigullapally, Harshithanjani Athi, Nikhil Karamchandani +1
We consider a distributed multi-user secret sharing (DMUSS) setting in which there is a dealer, storage nodes, and secrets. Each user demands a -subset of secrets. E…
Fixed confidence community mode estimation
Meera Pai, Nikhil Karamchandani, Jayakrishnan Nair
Our aim is to estimate the largest community (a.k.a., mode) in a population composed of multiple disjoint communities. This estimation is performed in a fixed confidence setting vi…