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..
EigenGame Unloaded: When playing games is better than optimizing
Authors: Ian Gemp, Brian McWilliams, Claire Vernade, Thore Graepel
ICLR 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate its performance with extensive experiments including dimensionality reduction of massive data sets and clustering a large social network graph. |
| Researcher Affiliation | Industry | Ian Gemp , Brian Mc Williams , Claire Vernade & Thore Graepel Deep Mind, London UK EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 presents pseudocode for µ-Eigen Game where computation is parallelized over the k players. |
| Open Source Code | No | For the sake of reproducibility we have included pseudocode in Jax. We use the Optax optimization library Hessel et al. (2020) and the Jaxline training framework. |
| Open Datasets | Yes | We compare µ-Eigen Game against α-Eigen Game, GHA (Sanger, 1989), Matrix Krasulina (Tang, 2019), and Oja s algorithm (Allen-Zhu and Li, 2017) on the MNIST dataset. ... The dataset consists a subset of the 40 billion words used to train the transformer-based Meena language model (Adiwardana et al., 2020). ... The Facebook graph consists of 134, 833 nodes, 1, 380, 293 edges, and 8 connected components... (Leskovec and Krevl, 2014; Rozemberczki et al., 2019). |
| Dataset Splits | No | For MNIST, it states 'Learning rates were chosen from {10 3, . . . , 10 6} on 10 held out runs,' which implies hyperparameter tuning, but it does not specify explicit training/validation/test dataset split percentages or sample counts. It refers to a 'training set' but provides no details on how it was split. |
| Hardware Specification | Yes | Specifically we consider the parallel framework specified by TPUv3 available in Google Cloud... We use minibatches of size 4,096 in each TPU. We do model parallelism across 4 TPUs... The experiment was run on a single CPU. |
| Software Dependencies | No | The paper mentions using 'Optax optimization library' and 'Jaxline training framework' but does not provide specific version numbers for these or other software dependencies. |
| Experiment Setup | Yes | We use minibatches of size 4,096 in each TPU. We compute and apply updates using SGD with a learning rate of 5 10 5 and Nesterov momentum with a factor of 0.9. ... Learning rates were chosen from {10 3, . . . , 10 6} on 10 held out runs. |