5 citations · 15 across the 17 of their papers we have counts for
19 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 Sample Efficient Alternating Minimization-based Algorithm For Robust Phase Retrieval
Adarsh Barik, Anand Krishna, Vincent Y. F. Tan
In this work, we study the robust phase retrieval problem where the task is to recover an unknown signal in the presence of potentially arbitrarily corrupted…
LEARN: An Invex Loss for Outlier Oblivious Robust Online Optimization
Adarsh Barik, Anand Krishna, Vincent Y. F. Tan
We study a robust online convex optimization framework, where an adversary can introduce outliers by corrupting loss functions in an arbitrary number of rounds k, unknown to the le…
Invex Programs: First Order Algorithms and Their Convergence
Adarsh Barik, Suvrit Sra, Jean Honorio
Invex programs are a special kind of non-convex problems which attain global minima at every stationary point. While classical first-order gradient descent methods can solve them,…