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

Ensemble methods that train multiple learners and then combine them to use, with Boosting
and Bagging as representatives, are well-known machine learning approaches. It has …

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 introduce data-driven decision-making algorithms that achieve state-of-the-art dynamic
regret bounds for a collection of nonstationary stochastic bandit settings. These settings …

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

The standard supervised learning paradigm works effectively when training data shares the
same distribution as the upcoming testing samples. However, this stationary assumption is …

Causal effect identification is a fundamental task in artificial intelligence. A most ideal
scenario for causal effect identification is that there is a directed acyclic graph as a prior …

Non-stationary online learning has drawn much attention in recent years. In particular,\emph
{dynamic regret} and\emph {adaptive regret} are proposed as two principled performance …

This paper introduces the notion of upper-linearizable/quadratizable functions, a class that
extends concavity and DR-submodularity in various settings, including monotone and non …