Multi-group Agnostic PAC Learnability
Authors: Guy N Rothblum, Gal Yona
ICML 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our main technical contributions answer both questions: 1. We prove a partial information-theoretic characterization of the compatible loss functions. 2. For any such loss function that also satisfies a natural uniform convergence property, we show an algorithm that, for any specified finite collection G and finite hypothesis class H, learns a multi-group agnostic predictor from labeled data. |
| Researcher Affiliation | Academia | 1Weizmann Institute of Science, Rehovot, Israel. |
| Pseudocode | Yes | Algorithm 1 Multi Group L,f(ϵ, δ, γ, H, G) and Algorithm 2 AL,f,k g,h,α (multi-sample Sample-Access OI distinguisher) |
| Open Source Code | No | The paper does not mention any open-source code for the methodology described. |
| Open Datasets | No | The paper does not describe the use of any specific dataset for training, as it focuses on theoretical analysis rather than empirical evaluation. |
| Dataset Splits | No | The paper is theoretical and does not describe empirical experiments, therefore no training/validation/test dataset splits are provided. |
| Hardware Specification | No | The paper is theoretical and does not conduct experiments, so no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not describe implementation details or empirical experiments, therefore no software dependencies with specific version numbers are provided. |
| Experiment Setup | No | The paper is theoretical and does not describe empirical experiments, thus no experimental setup details like hyperparameters or training settings are provided. |