Conventional machine learning studies generally assume close-environment scenarios
where important factors of the learning process hold invariant. With the great success of …

We investigate online convex optimization in non-stationary environments and choose the
dynamic regret as the performance measure, defined as the difference between cumulative …

This paper investigates the problem of non-stationary linear bandits, where the unknown
regression parameter is evolving over time. Previous studies have adopted sophisticated …

We investigate online convex optimization in non-stationary environments and choose
dynamic regret as the performance measure, defined as the difference between cumulative …

Learning with streaming data has attracted much attention during the past few years.
Though most studies consider data stream with fixed features, in real practice the features …

We study the problem of Online Convex Optimization (OCO) with memory, which allows loss
functions to depend on past decisions and thus captures temporal effects of learning …

In many real-world applications, data are often collected in the form of a stream, and thus the
distribution usually changes in nature, which is referred to as concept drift in the literature …

In many real-world applications, data are collected in the form of a stream, whose feature
space can evolve over time. For instance, in the environmental monitoring task, features can …

Bandit Convex Optimization (BCO) is a fundamental framework for modeling sequential
decision-making with partial information, where the only feedback available to the player is …

In performance tests, replacing humans with robotic drivers has many advantages, such as
high efficiency and high security. To realize the vehicle speed tracking control with a robotic …