88 citations · 171 across the 39 of their papers we have counts for
4 papers · 1 filter
Collaborative Learning in Kernel-based Bandits for Distributed Users
Sudeep Salgia, Sattar Vakili, Qing Zhao
We study collaborative learning among distributed clients facilitated by a central server. Each client is interested in maximizing a personalized objective function that is a weigh…
Near-Optimal Collaborative Learning in Bandits
Clémence Réda, Sattar Vakili, Emilie Kaufmann
This paper introduces a general multi-agent bandit model in which each agent is facing a finite set of arms and may communicate with other agents through a central controller in or…
Provably and Practically Efficient Neural Contextual Bandits
Sudeep Salgia, Sattar Vakili, Qing Zhao
We consider the neural contextual bandit problem. In contrast to the existing work which primarily focuses on ReLU neural nets, we consider a general set of smooth activation funct…
Improved Convergence Rates for Sparse Approximation Methods in Kernel-Based Learning
Sattar Vakili, Jonathan Scarlett, Da-shan Shiu +1
Kernel-based models such as kernel ridge regression and Gaussian processes are ubiquitous in machine learning applications for regression and optimization. It is well known that a…