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..
Extra-gradient with player sampling for faster convergence in n-player games
Authors: Samy Jelassi, Carles Domingo-Enrich, Damien Scieur, Arthur Mensch, Joan Bruna
ICML 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirically, we ο¬rst validate that DSEG is faster in massive differentiable convex games with noisy gradient oracles. We further show that non-random player selection improves convergence speed, and provide explanations for this phenomenon. In practical non-convex settings, we ο¬nd that cyclic player sampling improves the speed and performance of GAN training (CIFAR10, Res Net architecture). |
| Researcher Affiliation | Collaboration | Samy Jelassi * 1 Carles Domingo-Enrich * 2 Damien Scieur 3 Arthur Mensch 4 2 Joan Bruna 2 ... 1Princeton University, USA 2NYU CIMS, New York, USA 3Samsung SAIT AI Lab, Montreal, Canada 4ENS, DMA, Paris, France. |
| Pseudocode | Yes | Algorithm 1 Doubly-stochastic extra-gradient. ... Algorithm 2 Variance reduced estimate of the simultaneous gradient with doubly-stochastic sampling |
| Open Source Code | Yes | A Py Torch/Numpy package is attached. |
| Open Datasets | Yes | We evaluate the performance of the player sampling approach to train a generative model on CIFAR10 (Krizhevsky & Hinton, 2009). |
| Dataset Splits | No | No explicit information on training, validation, or test dataset splits (e.g., 80/10/10 percentages or specific sample counts) was found in the paper. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, cloud instances with specifications) used for running experiments were found in the paper. |
| Software Dependencies | No | A Py Torch/Numpy package is attached. |
| Experiment Setup | Yes | We selected all hyperparameters (stepsize and batch size for the number of sampled players) through a grid search for each experimental setting... Learning rates were selected by grid searching over {1eβ5, 3eβ5, 5eβ5, 1eβ4, 3eβ4, 5eβ4} for each of the methods. |