Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Multi-group Agnostic PAC Learnability
Authors: Guy N Rothblum, Gal Yona
ICML 2021 | Venue PDF | 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. |