7 papers
A Simple Reduction Scheme for Constrained Contextual Bandits with Adversarial Contexts via Regression
Dhruv Sarkar, Abhishek Sinha
We study constrained contextual bandits (CCB) with adversarially chosen contexts, where each action yields a random reward and incurs a random cost. We adopt the standard realizabi…
Optimal Anytime Algorithms for Online Convex Optimization with Adversarial Constraints
Dhruv Sarkar, Abhishek Sinha
We propose an anytime online algorithm for the problem of learning a sequence of adversarial convex cost functions while approximately satisfying another sequence of adversarial on…
Revisiting Social Welfare in Bandits: UCB is (Nearly) All You Need
Dhruv Sarkar, Nishant Pandey, Sayak Ray Chowdhury
Regret in stochastic multi-armed bandits traditionally measures the difference between the highest reward and either the arithmetic mean of accumulated rewards or the final reward.…
Online Learning for Approximately-Convex Functions with Long-term Adversarial Constraints
Dhruv Sarkar, Samrat Mukhopadhyay, Abhishek Sinha
We study an online learning problem with long-term budget constraints in the adversarial setting. In this problem, at each round , the learner selects an action from a convex de…
DP-NCB: Privacy Preserving Fair Bandits
Dhruv Sarkar, Nishant Pandey, Sayak Ray Chowdhury
Multi-armed bandit algorithms are fundamental tools for sequential decision-making under uncertainty, with widespread applications across domains such as clinical trials and person…
TAPS : Frustratingly Simple Test Time Active Learning for VLMs
Dhruv Sarkar, Aprameyo Chakrabartty, Bibhudatta Bhanja
Test-Time Optimization enables models to adapt to new data during inference by updating parameters on-the-fly. Recent advances in Vision-Language Models (VLMs) have explored learni…