Aleksandrs Slivkins

Aleksandrs Slivkins is a Microsoft Research researcher and author of Introduction to Multi-Armed Bandits, an introductory textbook-style treatment of bandit algorithms and their connections to machine learning, algorithms, statistics, operations research, and economics.

Key facts

• Type: Researcher / author
• Institution in source: Microsoft Research NYC ^[src-019]
• Known here for: Introduction to Multi-Armed Bandits, first drafted in 2017, published in 2019, and revised online through April 2024 ^[src-019]
• Subject area: Multi Armed Bandits, including Stochastic Bandits, Thompson Sampling, Contextual Bandits, Adversarial Bandits, and Bandits With Knapsacks ^[src-019]

What it does

In the source, Slivkins frames multi-armed bandits as a teachable family of online decision-making problems rather than a single algorithm. The book decomposes the field into lines of work and presents each as a self-contained technical introduction with pointers to deeper literature ^[src-019].

The practitioner value of the book is its structure: it separates IID reward settings, Bayesian priors, similarity information, adversarial rewards, contextual signals, and economic constraints. That makes it useful as a map for choosing which bandit model fits a product, marketplace, experimentation, or allocation problem ^[src-019].

Related

• See also: Multi Armed Bandits, Exploration Exploitation Tradeoff, Stochastic Bandits, Thompson Sampling, Contextual Bandits, Adversarial Bandits, Bandits With Knapsacks

Source references

• ^[src-019] Aleksandrs Slivkins — "Introduction to Multi-Armed Bandits" (2019-04-15; revised 2024-04-03)