Abstract Recommender Systems (RSs) have assumed a crucial role in several digital
companies by directly affecting their key performance indicators. Nowadays, in this era of big …

In this paper, we study Federated Bandit, a decentralized Multi-Armed Bandit problem with a
set of N agents, who can only communicate their local data with neighbors described by a …

We survey distributed deep learning models for training or inference without accessing raw
data from clients. These methods aim to protect confidential patterns in data while still …

This paper investigates the problem of regret minimization for multi-armed bandit (MAB)
problems with local differential privacy (LDP) guarantee. In stochastic bandit systems, the …

Bayesian optimization is a powerful tool for fine-tuning the hyper-parameters of a wide
variety of machine learning models. The success of machine learning has led practitioners …

Data that is gathered adaptively—via bandit algorithms, for example—exhibits bias. This is
true both when gathering simple numeric valued data—the empirical means kept track of by …

Abstract 1 We study the problem of privacy for distributed learning in Multi-Armed bandit
(MAB) problem with multiple players. The players must co-ordinate, as choosing the same …

Abstract Nowadays, Recommendation Systems (RSs) have been concerned about the
online environment of real-world applications where the system should continually learn and …