Summary of Omnipredicting Single-index Models with Multi-index Models, by Lunjia Hu et al.
Omnipredicting Single-Index Models with Multi-Index Models
by Lunjia Hu, Kevin Tian, Chutong Yang
First submitted to arxiv on: 20 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Data Structures and Algorithms (cs.DS); Optimization and Control (math.OC); 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 This paper explores omniprediction, a concept that seeks to approximate the Bayes-optimal predictor by minimizing a family of loss functions against a comparator class. The authors build upon recent work in supervised learning [GKR+22] and aim to develop practical omnipredictor constructions for basic settings like single-index models (SIMs). However, existing methods require large sample complexities and runtimes, and output complex hypotheses. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In simple terms, this paper is about finding a way to make predictions that are as good as possible, without knowing what the “best” prediction looks like. The goal is to create a method that can work well even when we don’t have much data or when the problem is tricky. The current methods for doing this are not very efficient and produce complex results. |
Keywords
» Artificial intelligence » Supervised
