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An Overview of Contextual Bandits
3 Feb 2024
towardsdatascience.com

A dynamic approach to treatment personalization

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Applying Reinforcement Learning strategies to real-world use cases, especially in dynamic pricing, can reveal many surprises

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Delve deeper into the concept of multi-armed bandits, reinforcement learning, and exploration vs. exploitation dilemma.

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As of 2024, The Data Incubator is now Pragmatic Data! Explore Pragmatic Institute’s new offerings, learn about team training opportunities, and more.

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Multi-armed bandits for dynamic movie recommendations
18 Jul 2022
blog.insightdatascience.com

Making the best recommendations to anonymous audiences

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Multi-Armed Bandit Algorithms: Thompson Sampling
27 Mar 2022
towardsdatascience.com

Intuition, Bayes, and an example

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Library for multi-armed bandit selection strategies, including efficient deterministic implementations of Thompson sampling and epsilon-greedy. - stitchfix/mab

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Thompson Sampling using Conjugate Priors
10 Mar 2021
towardsdatascience.com

Multi-Armed Bandits: Part 5b

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A Comparison of Bandit Algorithms
10 Nov 2020
towardsdatascience.com

Multi-Armed Bandits: Part 6

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We've recently built support for multi-armed bandits into the Stitch Fix experimentation platform. This post will explain how and why.

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Bandit Algorithms
2 Nov 2020
towardsdatascience.com

Multi-Armed Bandits: Part 3

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Introducing the multi-armed bandit (MAB) optimization used for hero images on hotels.com

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Multi-armed bandits are a simple but very powerful framework for algorithms that make decisions over time under uncertainty. “Introduction to Multi-Armed Bandits” by Alex Slivkins provides an accessible, textbook-like treatment of the subject.

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There are multiple ways to doing the same thing in Pandas, and that might make it troublesome for the beginner user.This post is about handling most of the data manipulation cases in Python using a straightforward, simple, and matter of fact way.

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Bandit Algorithms
31 Aug 2018
banditalgs.com