Opportunistic spectrum access (OSA) has been regarded as the most promising approach to
solve the paradox between spectrum scarcity and waste. Intelligent decision making is key …

Decision-making in the face of uncertainty is a significant challenge in machine learning,
and the multi-armed bandit model is a commonly used framework to address it. This …

By exploiting the computing power and local data of distributed clients, federated learning
(FL) features ubiquitous properties such as reduction of communication overhead and …

We define a general framework for a large class of combinatorial multi-armed bandit (CMAB)
problems, where simple arms with unknown istributions form\em super arms. In each round …

A search engine usually outputs a list of K web pages. The user examines this list, from the
first web page to the last, and chooses the first attractive page. This model of user behavior …

A stochastic combinatorial semi-bandit is an online learning problem where at each step a
learning agent chooses a subset of ground items subject to constraints, and then observes …

The multi-armed bandit (MAB) model has been widely adopted for studying many practical
optimization problems (network resource allocation, ad placement, crowdsourcing, etc.) with …

In order to meet the growing needs for wireless communication in dynamic and diverse
circumstances, Cognitive Radio Networks (CRNs) have evolved as a transformational …

In the past few years, differential privacy has become a standard concept in the area of
privacy. One of the most important problems in this field is to answer queries while …

This paper investigates stochastic and adversarial combinatorial multi-armed bandit
problems. In the stochastic setting under semi-bandit feedback, we derive a problem-specific …