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..
Distributed Saddle-Point Problems Under Data Similarity
Authors: Aleksandr Beznosikov, Gesualdo Scutari, Alexander Rogozin, Alexander Gasnikov
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We simulate the Robust Linear Regression problem... We assess the effectiveness of the proposed algorithms on a robust regression problem. Figure 1 compares the performance of Algorithm 1 and the Centralized Extragradient method... Our second experiment is using real data, specifically LIBSVM datasets [8]. |
| Researcher Affiliation | Collaboration | Aleksandr Beznosikov MIPT , HSE University and Yandex, Russia Gesualdo Scutari Purdue University, USA Alexander Rogozin MIPT and HSE University, Russia Alexander Gasnikov MIPT, HSE University and ISP RAS , Russia |
| Pseudocode | Yes | Algorithm 1 (Star Min-Max Data Similarity Algorithm) ... Algorithm 2 (Distributed Min-Max Data Similarity Algorithm) ... Algorithm 3 (Acc Gossip) |
| Open Source Code | Yes | Source code: https://github.com/alexrogozin12/data_sim_sp |
| Open Datasets | Yes | Our second experiment is using real data, specifically LIBSVM datasets [8]. |
| Dataset Splits | No | No specific training/validation/test dataset splits (percentages, counts, or explicit standard split citations) were found in the paper's main text for reproducibility. |
| Hardware Specification | No | No specific hardware details (like GPU/CPU models, processor types, or memory amounts) used for running the experiments were found in the paper. |
| Software Dependencies | Yes | The algorithms are implemented in Python 3.73. |
| Experiment Setup | Yes | A description of the tuning of the algorithm parameters can be found in Appendix C. |