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.
The Symmetric Generalized Eigenvalue Problem as a Nash Equilibrium
Authors: Ian Gemp, Charlie Chen, Brian McWilliams
ICLR 2023 | Venue PDF | LLM Run Details | Input Tokens: 17,801 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,383 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 | Empirically we demonstrate that this resulting algorithm is able to solve a variety of SGEP problem instances including a large-scale analysis of neural network activations. |
| Researcher Affiliation | Industry | EMAIL Charlie Chen EMAIL Brian Mc Williams Google Research Z urich, Switzerland EMAIL |
| Pseudocode | Yes | Algorithm 1 Deterministic / Full-batch γ-Eigen Game; Algorithm 2 Stochastic γ-Eigen Game |
| Open Source Code | Yes | A Jax implementation is available at github.com/deepmind/eigengame. |
| Open Datasets | Yes | We replicate a synthetic experiment from scikit-learn(Pedregosa et al., 2011) and compare Algorithm 2 to several approaches... loading minibatches of CIFAR-10 images, running them through a deep convolutional network, harvesting the activations, and then passing them to our distributed γ-Eigen Game solver. |
| Dataset Splits | No | The paper mentions using minibatches and datasets like CIFAR-10, but it does not specify explicit training, validation, or test dataset splits (e.g., 80/10/10 percentages or sample counts). |
| Hardware Specification | Yes | Figure 4 demonstrates our approach (parallelized over 8 TPU chips) |
| Software Dependencies | No | The paper mentions using 'Jax', 'Scipy's linalg.eigh(A, B)', and 'scikit-learn', but it does not provide specific version numbers for these software components. |
| Experiment Setup | Yes | Hyperparameters are listed in Appx. H. |