From the 1 of 10 linked papers with an AI index.
10 papers
A Geometric Approach to Constrained Online Learning
Dhruv Sarkar, Abhishek Sinha
The paper introduces NP-OGD, a nested‑projection algorithm for online convex optimization with time‑varying constraints, achieving optimal regret and improved bounds on cumulative…
Constrained Contextual Bandits with Adversarial Contexts
Dhruv Sarkar, Abhishek Sinha
We study budget-constrained contextual bandits with adversarial contexts, where each action yields a random reward and incurs a random cost. We adopt the standard realizability ass…
Projection-free Algorithms for Online Convex Optimization with Adversarial Constraints
Dhruv Sarkar, Aprameyo Chakrabartty, Subhamon Supantha +2
We study a generalization of the Online Convex Optimization (OCO) framework with time-varying adversarial constraints. In this setting, at each round, the learner selects an action…
Universal Dynamic Regret and Constraint Violation Bounds for Constrained Online Convex Optimization
Subhamon Supantha, Abhishek Sinha
We consider a generalization of the celebrated Online Convex Optimization (OCO) framework with adversarial online constraints. In this problem, an online learner interacts with an…
Beyond Constraint Violation for Online Convex Optimization with Adversarial Constraints
Abhishek Sinha, Rahul Vaze
We study Online Convex Optimization with adversarial constraints (COCO). At each round a learner selects an action from a convex decision set and then an adversary reveals a convex…
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…