This paper investigates the logistic bandit problem, a variant of the generalized linear bandit
model that utilizes a logistic model to depict the feedback from an action. While most existing …

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

In this paper, we propose an online convex optimization approach with two different levels of
adaptivity. On a higher level, our approach is agnostic to the unknown types and curvatures …

In many practical applications, such as poker, chess, drug interdiction, cybersecurity, and
national defense, players often have adversarial stances, ie, the selfish actions of each …

Most of the literature on learning in games has focused on the restrictive setting where the
underlying repeated game does not change over time. Much less is known about the …

Data-free quantization (DFQ) recovers the performance of quantized network (Q) without
accessing the real data, but generates the fake sample via a generator (G) by learning from …

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 …

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

Last-iterate convergence has received extensive study in two player zero-sum games
starting from bilinear, convex-concave up to settings that satisfy the MVI condition. Typical …

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 …