Summary of Online Decision Deferral Under Budget Constraints, by Mirabel Reid et al.
Online Decision Deferral under Budget Constraints
by Mirabel Reid, Tom Sühr, Claire Vernade, Samira Samadi
First submitted to arxiv on: 30 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 This paper proposes a contextual bandit model for online decision-making problems where machine learning (ML) models are used to support or substitute expert decisions. The goal is to reduce the burden on experts and automate decisions when ML models perform equally well. However, as tasks arrive sequentially with shifting distributions, the algorithm must remain adaptive while considering budget constraints and partial feedback types. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study focuses on developing a framework for online decision making that uses machine learning models to help or replace expert decisions. The main challenge is creating an algorithm that can adapt to changing task distributions while keeping in mind budget limitations and different types of feedback. The researchers propose a contextual bandit model that has the potential to perform well in real-world scenarios. |
Keywords
» Artificial intelligence » Machine learning
