Federated multi-armed bandits (FMAB) is a new bandit paradigm that parallels the federated
learning (FL) framework in supervised learning. It is inspired by practical applications in …

Over the past two decades, several studies have paid great attention to biometric palmprint
recognition. Recently, most methods in literature adopted deep learning due to their high …

-means method using Lloyd heuristic is a traditional clustering method which has played a
key role in multiple downstream tasks of machine learning because of its simplicity …

Linear contextual bandit is a popular online learning problem. It has been mostly studied in
centralized learning settings. With the surging demand of large-scale decentralized model …

Open Set Recognition (OSR) is about dealing with unknown situations that were not learned
by the models during training. In this paper, we provide a survey of existing works about …

Recommender systems (RS) play a vital role in daily life due to their practical significance.
As a branch of RS, the sequential recommendation has attracted much attention because of …

Real-world recommendation systems need to deal with millions of item candidates.
Therefore, most practical large-scale recommendation systems usually contain two modules …

Conversational recommender systems (CRSs) have been proposed recently to mitigate the
cold-start problem suffered by the traditional recommender systems. By introducing …

We create a computationally tractable learning algorithm for contextual bandits with
continuous actions having unknown structure. The new reduction-style algorithm composes …

Optimization of reactive power dispatch (ORPD) problem is a key factor for stable and
secure operation of the electric power systems. In this paper, a newly explored nature …