Summary of Contextual Bandits with Stage-wise Constraints, by Aldo Pacchiano et al.
Contextual Bandits with Stage-wise Constraints
by Aldo Pacchiano, Mohammad Ghavamzadeh, Peter Bartlett
First submitted to arxiv on: 15 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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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 tackle contextual bandits under stage-wise constraints, focusing on balancing exploration and constraint satisfaction in linear and non-linear settings. They develop upper-confidence bound algorithms, proving regret bounds and lower-bounds for constrained problems. The study extends to multi-armed bandits, proposing efficient algorithms with regret analysis. The researchers also discuss minimum requirements for action sets and explore the eluder dimension’s impact on function class complexity. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using machines to make good decisions in situations where you need to follow certain rules. Imagine playing a game where you have to choose between different options, but each option has its own set of rules you must follow. The researchers came up with new ways to play this game while following the rules and still making smart choices. |
