A radically new approach to statistical modelling, which combines mathematical techniques
of Bayesian statistics with the philosophy of the theory of competitive on‐line algorithms, has …

Aggregated predictors are obtained by making a set of basic predictors vote according to
some weights, that is, to some probability distribution. Randomized predictors are obtained …

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 …

Online learning is a framework for the design and analysis of algorithms that build predictive
models by processing data one at the time. Besides being computationally efficient, online …

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 …

The regression estimation problem has a long history. Already in 1632 Galileo Galilei used a
procedure which can be interpreted as? tting a linear relationship to contaminated observed …

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 …

The mathematical theory of machine learning not only explains the current algorithms but
can also motivate principled approaches for the future. This self-contained textbook …

Personal motivation. The dream of creating artificial devices that reach or outperform human
inteUigence is an old one. It is also one of the dreams of my youth, which have never left me …