A fundamental challenge in interactive learning and decision making, ranging from bandit
problems to reinforcement learning, is to provide sample-efficient, adaptive learning …

Online prediction methods are typically presented as serial algorithms running on a single
processor. However, in the age of web-scale prediction problems, it is increasingly common …

Bandit convex optimization is a special case of online convex optimization with partial
information. In this setting, a player attempts to minimize a sequence of adversarially …

Beyond the Regret Minimization Barrier: Optimal Algorithms for Stochastic Strongly-Convex
Optimization Page 1 Journal of Machine Learning Research 15 (2014) 2489-2512 Submitted …

In this paper we propose efficient algorithms for solving constrained online convex
optimization problems. Our motivation stems from the observation that most algorithms …

This article proposes a novel online portfolio selection strategy named “Passive Aggressive
Mean Reversion”(PAMR). Unlike traditional trend following approaches, the proposed …

To cope with changing environments, recent developments in online learning have
introduced the concepts of adaptive regret and dynamic regret independently. In this paper …

We give a novel algorithm for stochastic strongly-convex optimization in the gradient oracle
model which returns an $ O (\frac1T) $-approximate solution after $ T $ gradient updates …

We establish necessary and sufficient conditions for a uniform martingale Law of Large
Numbers. We extend the technique of symmetrization to the case of dependent random …

Motivated by the challenge of nonstationarity in sequential decision making, we study Online
Convex Optimization (OCO) under the coupling of two problem structures: the domain is …