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1
Intro
2
Long Term Motivation
3
Environmental changes are frequent in practice ©
4
(Contextual) Bandits
5
Outline
6
Changes in Reward Y, distribution
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Key Contributions
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Intuition: various changes can safely be ignored
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Definition (Significant Phases P.)
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Adaptive Procedure
11
Designing Random Replays
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Changes in Context X, distribution
13
Usual (stationary) Strategies
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How informative is previous Py for Qx?
15
Summary
Description:
Explore the concept of tracking significant changes in bandit algorithms through this insightful 49-minute talk by Samory Kpotufe from Columbia University, presented at IFDS 2022. Delve into the long-term motivation behind addressing environmental changes in contextual bandits, a common occurrence in practical applications. Examine key contributions, including the intuition that various changes can be safely ignored and the definition of significant phases. Learn about adaptive procedures and the design of random replays. Investigate changes in context distribution and evaluate the informativeness of previous probability distributions. Gain a comprehensive understanding of strategies for dealing with non-stationary environments in bandit algorithms.

Tracking Significant Changes in Bandit - IFDS 2022

Paul G. Allen School
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