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..
Average-case Acceleration for Bilinear Games and Normal Matrices
Authors: Carles Domingo-Enrich, Fabian Pedregosa, Damien Scieur
ICLR 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We illustrate our findings through numerical simulations with a varying degree of mismatch with our assumptions. |
| Researcher Affiliation | Collaboration | Carles Domingo-Enrich Computer Science Department Courant Institute of Mathematical Sciences New York University New York, NY 10012, USA EMAIL Fabian Pedregosa Google Research EMAIL Damien Scieur Samsung SAIT AI Lab & Mila Montreal, Canada EMAIL |
| Pseudocode | Yes | Optimal average-case algorithm for bilinear games. Initialization. x 1 = x0 = θ1,0, θ2,0 , sequence {ht, mt} given by Theorem 3.1. Main loop. For t 0, gt = F(xt F(xt)) F(xt) = 1 2 F(xt) 2 by (12) xt+1 = xt ht+1gt + mt+1(xt 1 xt) (11) |
| Open Source Code | No | No explicit statement providing access to the source code for the methodology. |
| Open Datasets | No | We consider min-max bilinear problems of the form (10), where the entries of M are generated i.i.d. from a standard Gaussian distribution. No concrete access information for a publicly available or open dataset. |
| Dataset Splits | No | No specific dataset split information (percentages, counts, or explicit standard splits) is provided. |
| Hardware Specification | No | No specific hardware details (like GPU/CPU models, memory, or specific computing platforms) are mentioned for running experiments. |
| Software Dependencies | No | No specific ancillary software details with version numbers are provided. |
| Experiment Setup | No | No specific experimental setup details such as concrete hyperparameter values (e.g., learning rate, batch size, epochs) or detailed training configurations are provided. |