Online updating of time series forecasting models aims to address the concept drifting
problem by efficiently updating forecasting models based on streaming data. Many …

Multi-armed bandits a simple but very powerful framework for algorithms that make
decisions over time under uncertainty. An enormous body of work has accumulated over the …

This chapter focuses on providing a complete grasp of the foundations of machine learning
(ML). Machine learning is a rapidly evolving domain with wide-ranging applications, from …

In a multi-armed bandit (MAB) problem a gambler needs to choose at each round of play
one of K arms, each characterized by an unknown reward distribution. Reward realizations …

Multiagent systems combine multiple autonomous entities, each having diverging interests
or different information. This overview of the field offers a computer science perspective, but …

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 the multiarmed bandit problem, a gambler must decide which arm of K nonidentical slot
machines to play in a sequence of trials so as to maximize his reward. This classical …

Convex programming involves a convex set F⊆ Rn and a convex cost function c: F→ R. The
goal of convex programming is to find a point in F which minimizes c. In online convex …

We provide an overview of recent research on belief and opinion dynamics in social
networks. We discuss both Bayesian and non-Bayesian models of social learning and focus …

Algorithms in varied fields use the idea of maintaining a distribution over a certain set and
use the multiplicative update rule to iteratively change these weights. Their analyses are …