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..
On Sample Optimality in Personalized Collaborative and Federated Learning
Authors: Mathieu Even, Laurent Massoulié, Kevin Scaman
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We numerically illustrate our theory in Appendix A on synthetic datasets, with clustered agents (as in this section), as well as in a setting where agents are distributed according to a more general distribution of agent . |
| Researcher Affiliation | Collaboration | 1Inria Paris Département d informatique de l ENS, PSL Research University 2Microsoft-Inria Joint Center |
| Pseudocode | Yes | Algorithm 1 All-for-all algorithm |
| Open Source Code | No | No explicit statement about providing open-source code for the methodology described in this paper or a direct link to a code repository was found. |
| Open Datasets | Yes | for the MNIST dataset, deff is less than 3, while the ambient dimension is 712 [22]. |
| Dataset Splits | No | No specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) needed to reproduce the data partitioning was found. |
| Hardware Specification | No | No specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running experiments were provided. |
| Software Dependencies | No | No specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment were provided. |
| Experiment Setup | No | The paper does not provide specific experimental setup details such as concrete hyperparameter values, optimizer settings, or detailed training configurations for the algorithms. |