4 papers
Bandit Convex Optimization with Gradient Prediction Adaptivity
Shuche Wang, Adarsh Barik, Vincent Y. F. Tan
Bandit convex optimization (BCO) is a fundamental online learning framework with partial feedback, where the learner observes only the loss incurred at the chosen decision point in…
Parameter-free Algorithms for the Stochastically Extended Adversarial Model
Shuche Wang, Adarsh Barik, Peng Zhao +1
We develop the first parameter-free algorithms for the Stochastically Extended Adversarial (SEA) model, a framework that bridges adversarial and stochastic online convex optimizati…
p-Mean Regret for Stochastic Bandits
Anand Krishna, Philips George John, Adarsh Barik +1
In this work, we extend the concept of the -mean welfare objective from social choice theory (Moulin 2004) to study -mean regret in stochastic multi-armed bandit problems. Th…
A Novel Plug-and-Play Approach for Adversarially Robust Generalization
Deepak Maurya, Adarsh Barik, Jean Honorio
In this work, we propose a robust framework that employs adversarially robust training to safeguard the ML models against perturbed testing data. Our contributions can be seen from…