We study the problem of uncertainty quantification for time series prediction, with the goal of
providing easy-to-use algorithms with formal guarantees. The algorithms we present build …

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 …

Recent discussion in the public sphere about algorithmic classification has involved tension
between competing notions of what it means for a probabilistic classification to be fair to …

The combination of increased availability of large amounts of fine-grained human behavioral
data and advances in machine learning is presiding over a growing reliance on algorithms …

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 …

When a user has watched, say, 70 romance movies and 30 action movies, then it is
reasonable to expect the personalized list of recommended movies to be comprised of about …

Multi-armed bandit problems are the most basic examples of sequential decision problems
with an exploration-exploitation trade-off. This is the balance between staying with the option …

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 …

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 …