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 [1].
Competitive Gradient Descent
Authors: Florian Schaefer, Anima Anandkumar
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Using numerical experiments and rigorous analysis, we provide a detailed comparison to methods based on optimism and consensus and show that our method avoids making any unnecessary changes to the gradient dynamics while achieving exponential (local) convergence for (locally) convex-concave zero sum games. |
| Researcher Affiliation | Academia | Florian Schäfer Computing and Mathematical Sciences California Institute of Technology Pasadena, CA 91125 EMAIL Anima Anandkumar Computing and Mathematical Sciences California Institute of Technology Pasadena, CA 91125 EMAIL |
| Pseudocode | Yes | Algorithm 1: Competitive Gradient Descent (CGD) for 0 k N 1 do xk+1 = xk η Id η2D2 xyf D2 yxg 1 xf ηD2 xyf yg ; yk+1 = yk η Id η2D2 yxg D2 xyf 1 yg ηD2 yxg xf ; return (x N, y N); |
| Open Source Code | No | The paper mentions third-party libraries like Autograd and Julia Diff frameworks that support automatic differentiation, but it does not provide a link or statement for the authors' own implementation code. |
| Open Datasets | No | The paper mentions using a "Gaussian mixture model with two modes, in two dimensions (see supplement for details)" and a "covariance estimation example used by Daskalakis et al. (2017)[Appendix C]". While a citation is provided for the latter, neither is explicitly stated as a publicly available dataset with concrete access information (e.g., URL, DOI, or a clear statement of public access to the specific dataset used). |
| Dataset Splits | No | The paper does not provide specific details regarding dataset splits for training, validation, or testing (e.g., percentages, sample counts, or references to predefined splits). |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory, or specific cloud instances) used for running the experiments. |
| Software Dependencies | No | The paper mentions "Autograd", "Forward Diff", "Reverse Diff", and "RMSProp (ρ = 0.9)" but does not provide specific version numbers for these software dependencies, which are necessary for full reproducibility. |
| Experiment Setup | Yes | We apply SGA, Con Opt (γ = 1.0), OGDA, and CGD for stepsize η {0.4, 0.1, 0.025, 0.005} together with RMSProp (ρ = 0.9). |