Summary of Omnipredictors For Regression and the Approximate Rank Of Convex Functions, by Parikshit Gopalan et al.
Omnipredictors for Regression and the Approximate Rank of Convex Functions
by Parikshit Gopalan, Princewill Okoroafor, Prasad Raghavendra, Abhishek Shetty, Mihir Singhal
First submitted to arxiv on: 26 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computational Complexity (cs.CC); Data Structures and Algorithms (cs.DS)
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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 The paper introduces the concept of omnipredictors in the regression setting, where the goal is to learn to predict continuous labels given points from a distribution. An omnipredictor for a class of loss functions and a class of hypotheses is a predictor that incurs less expected loss than the best hypothesis for every loss function. The authors focus on the notion of sufficient statistics for loss minimization over a family of loss functions, which allows one to take actions that minimize the expected loss for any loss in the family. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In simple terms, this paper is about learning how to predict continuous values based on some given information. It’s trying to find the best way to make predictions that are close to what really happens, and it does this by looking at different ways of measuring how good or bad those predictions are. The main idea is that if you know certain things about a distribution of data, you can use that knowledge to make better predictions. |
Keywords
* Artificial intelligence * Loss function * Regression
