Online portfolio selection is a fundamental problem in computational finance, which has
been extensively studied across several research communities, including finance, statistics …

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

In this monograph, I introduce the basic concepts of Online Learning through a modern view
of Online Convex Optimization. Here, online learning refers to the framework of regret …

This monograph portrays optimization as a process. In many practical applications the
environment is so complex that it is infeasible to lay out a comprehensive theoretical model …

This important text and reference for researchers and students in machine learning, game
theory, statistics and information theory offers a comprehensive treatment of the problem of …

In an online convex optimization problem a decision-maker makes a sequence of decisions,
ie, chooses a sequence of points in Euclidean space, from a fixed feasible set. After each …

We consider a the general online convex optimization framework introduced by Zinkevich. In
this setting, there is a sequence of convex functions. Each period, we must choose a signle …

The class of logconcave functions in ℝn is a common generalization of Gaussians and of
indicator functions of convex sets. Motivated by the problem of sampling from logconcave …

In this paper, we consider the problem of preserving privacy in the context of online learning.
Online learning involves learning from data in real-time, due to which the learned model as …

Portfolio analysis and optimization, together with the associated risk assessment and
management, require knowledge of the likely distributions of returns at different time scales …