7 papers · 1 filter
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…
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…
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…
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…
Static Regret and Instance Dependent Constraint Violation for Constrained Online Convex Optimization
Rahul Vaze, Abhishek Sinha
The constrained version of the standard online convex optimization (OCO) framework, called COCO is considered, where on every round, a convex cost function and a convex constraint…
Online Bidding Algorithms with Strict Return on Spend (ROS) Constraint
Rahul Vaze, Abhishek Sinha
Auto-bidding problem under a strict return-on-spend constraint (ROSC) is considered, where an algorithm has to make decisions about how much to bid for an ad slot depending on the…
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…