Average-case Acceleration for Bilinear Games and Normal Matrices
Authors: Carles Domingo-Enrich, Fabian Pedregosa, Damien Scieur
ICLR 2021 | Conference PDF | Archive PDF | Plain Text | 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 cd2754@nyu.edu Fabian Pedregosa Google Research pedregosa@google.com Damien Scieur Samsung SAIT AI Lab & Mila Montreal, Canada damien.scieur@gmail.com |
| 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. |