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 …

We tackle the communication efficiency challenge of learning kernelized contextual bandits
in a distributed setting. Despite the recent advances in communication-efficient distributed …

In this paper, we consider high dimensional contextual bandit problems. Within this setting,
Thompson Sampling and its variants have been proposed and have been successfully …

Automated investment managers are increasingly popular in personal wealth management
due to their cost effectiveness, objectivity, and accessibility. However, it still suffers from …

Abstract Counterfactual Risk Minimization (CRM) is a framework for dealing with the logged
bandit feedback problem, where the goal is to improve a logging policy using offline data. In …

Despite the recent advances in communication-efficient distributed bandit learning, most
existing solutions are restricted to parametric models, eg, linear bandits and generalized …

We study a generalization of the problem of online learning in adversarial linear contextual
bandits by incorporating loss functions that belong to a reproducing kernel Hilbert space …

The contextual bandit problem is a theoretically justified framework with wide applications in
various fields. While the previous study on this problem usually requires independence …

By leveraging the representation power of deep neural networks, neural upper confidence
bound (UCB) algorithms have shown success in contextual bandits. To further balance the …

We study a generalization of the problem of online learning in adversarial linear contextual
bandits by incorporating loss functions that belong to a reproducing kernel Hilbert space …