Online learning represents a family of machine learning methods, where a learner attempts
to tackle some predictive (or any type of decision-making) task by learning from a sequence …

This article considers predicting future statuses of multiple agents in an online fashion by
exploiting dynamic interactions in the system. We propose a novel collaborative prediction …

Kernel-based methods exhibit well-documented performance in various nonlinear learning
tasks. Most of them rely on a preselected kernel, whose prudent choice presumes task …

Kernel-based regression represents an important family of learning techniques for solving
challenging regression tasks with non-linear patterns. Despite being studied extensively …

Online portfolio selection (OPS) has received increasing attention from machine learning
and quantitative finance communities. Despite their effectiveness, the pioneering OPS …

Kernel-basedlearning has well-documented merits in various machine learning tasks. Most
of the kernel-based learning approaches rely on a pre-selected kernel, the choice of which …

The shape, size and distribution of the microstructure could certainly reveal mechanical
properties. Therefore, it is important to segment the microstructure accurately. However, in …

In many real-world applications, data may dynamically expand over time in both volume and
feature dimensions. Besides, they are often collected in batches (also called blocks). We …

Multi-view learning reveals the latent correlation between different input modalities and has
achieved outstanding performances in many fields. Recent approaches aim to find a low …

This paper investigates online active learning in the setting of class-imbalanced data
streams, where labels are allowed to be queried of with limited budgets. In this setup …