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..
Unravelling in Collaborative Learning
Authors: Aymeric Capitaine, Etienne Boursier, Antoine Scheid, Eric Moulines, Michael Jordan, El-Mahdi El-Mhamdi, Alain Durmus
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We provide a rigorous framework for analyzing collaborative learning with strategic agents having data distributions of varying quality. ... We show that when data quality is private, a naive aggregation strategy... results in a complete unravelling. ... We present solutions to unravelling. ... Theorem 1. Assume H1, H2, H3. Problem (8) admits a unique solution... Theorem 2 (Unravelling). Assume H1, H2, H3, and H4. Let E S J be the set of pure-strategy Nash equilibria... Theorem 3. Assume H1, H2, H3, H4 and H5. B = (1, . . . , 1)T is a Nash equilibrium under ˆΓ with probability 1 δ. |
| Researcher Affiliation | Academia | Aymeric Capitaine1 Etienne Boursier2 Antoine Scheid1 Eric Moulines1 Michael I. Jordan3 El-Mahdi El-Mhamdi1 Alain Durmus1 1 Centre de Mathématiques Appliquées CNRS École polytechnique Palaiseau, 91120, France 2 INRIA Saclay, Université Paris Saclay, LMO Orsay, 91400, France 3 Inria, Ecole Normale Supérieure, PSL Research University Paris, 75, France |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. Methods are described in prose and mathematical notation. |
| Open Source Code | No | The paper does not contain any explicit statement or link providing concrete access to source code for the methodology described. |
| Open Datasets | No | The paper describes a theoretical framework and does not conduct experiments on specific public or open datasets. The concept of 'samples' refers to theoretical data generation within the model. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical validation with dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper does not describe any experiments or their computational execution, thus no hardware specifications are provided. |
| Software Dependencies | No | The paper does not describe any experiments or their computational execution, thus no specific software dependencies with version numbers are provided. |
| Experiment Setup | No | The paper is theoretical and does not contain details about an experimental setup, hyperparameters, or system-level training settings. |