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 …

Generating learning-friendly representations for points in space is a fundamental and long-
standing problem in machine learning. Recently, multi-scale encoding schemes (such as …

Real data are often with multiple modalities or from multiple heterogeneous sources, thus
forming so-called multi-view data, which receives more and more attentions in machine …

Spatial representation learning (SRL) aims at learning general-purpose neural network
representations from various types of spatial data (eg, points, polylines, polygons, networks …

Learning with streaming data has received extensive attention during the past few years.
Existing approaches assume that the feature space is fixed or changes by following explicit …

Learning with streaming data has received extensive attention during the past few years.
Existing approaches assume the feature space is fixed or changes by following explicit …

Autonomous vehicle systems (AVSs) are widely used to transfer wafers in semiconductor
manufacturing. However, in such systems, robust traffic control is a significant challenge …

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

This paper studies the decentralized optimization and learning problem where multiple
interconnected agents aim to learn an optimal decision function defined over a reproducing …

The statistical characteristics of instance-label pairs often change with time in practical
scenarios of supervised classification. Conventional learning techniques adapt to such …