Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Optimal Algorithms for Decentralized Stochastic Variational Inequalities
Authors: Dmitry Kovalev, Aleksandr Beznosikov, Abdurakhmon Sadiev, Michael Persiianov, Peter Richtarik, Alexander Gasnikov
NeurIPS 2022 | Venue PDF | LLM Run Details | Input Tokens: 20,298 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,079 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Numerical experiments on bilinear problems and robust regression problems confirm the practical efficiency of our methods, both in the non-distributed stochastic setup and in the decentralized deterministic one. |
| Researcher Affiliation | Collaboration | Dmitry Kovalev KAUST , Saudi Arabia EMAIL Aleksandr Beznosikov MIPT , HSE University and Yandex, Russia EMAIL Abdurakhmon Sadiev MIPT, Russia EMAIL Michael Persiianov MIPT, Russia EMAIL Peter Richtárik KAUST, Saudi Arabia EMAIL Alexander Gasnikov MIPT, HSE University and IITP RAS , Russia EMAIL |
| Pseudocode | Yes | Algorithm 1 |
| Open Source Code | No | we run simple experiments just for theoretical purpose, it is easy to rerun it |
| Open Datasets | Yes | We take datasets from Li BSVM [19] and divided unevenly across M = 25 workers. For communication networks we chose the star, the ring and the grid topologies. |
| Dataset Splits | No | No specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) is provided for train/validation/test sets. |
| Hardware Specification | No | we run simple experiments just for theoretical purpose, they require no computing power and can be run on any laptop |
| Software Dependencies | No | The paper mentions 'LibSVM' as a source for datasets but does not provide specific version numbers for any software dependencies used in the experiments. |
| Experiment Setup | Yes | The parameters of all methods are selected in two ways: 1) as described in the theory of the corresponding papers, and 2) tuned for the best convergence. We run all methods with different batch sizes. The comparison criterion is the number of epochs (one full gradient = epoch). |