Summary of Causally Abstracted Multi-armed Bandits, by Fabio Massimo Zennaro et al.
Causally Abstracted Multi-armed Bandits
by Fabio Massimo Zennaro, Nicholas Bishop, Joel Dyer, Yorgos Felekis, Anisoara Calinescu, Michael Wooldridge, Theodoros Damoulas
First submitted to arxiv on: 26 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers extend the concept of transfer learning for causal multi-armed bandits (CMABs) to handle setups where CMABs are defined on potentially different variables with varying degrees of granularity. They introduce the problem of causally abstracted MABs (CAMABs), which relies on the theory of causal abstraction. The authors propose algorithms for learning in a CAMAB and analyze their regret. They demonstrate the limitations and strengths of these algorithms through a real-world scenario related to online advertising. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making better decisions when faced with multiple problems that are connected. Usually, we focus on one problem at a time, but sometimes we need to think about multiple problems together. The researchers want to help us do this by developing new methods for learning and decision-making in these complex situations. They’re using techniques from machine learning and causality theory to develop new algorithms for making decisions when there are many variables involved. |
Keywords
» Artificial intelligence » Machine learning » Transfer learning
