Distributed Saddle-Point Problems Under Data Similarity
Authors: Aleksandr Beznosikov, Gesualdo Scutari, Alexander Rogozin, Alexander Gasnikov
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | 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. |