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..
Stochastic Hamiltonian Gradient Methods for Smooth Games
Authors: Nicolas Loizou, Hugo Berard, Alexia Jolicoeur-Martineau, Pascal Vincent, Simon Lacoste-Julien, Ioannis Mitliagkas
ICML 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We supplement our analysis with experiments on stochastic bilinear and sufficiently bilinear games, where our theory is shown to be tight, and on simple adversarial machine learning formulations. (Abstract) |
| Researcher Affiliation | Collaboration | 1Mila, Universit e de Montr eal Canada CIFAR AI Chair 2Facebook AI Research. Correspondence to: Nicolas Loizou <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 Stochastic Hamiltonian Gradient Descent (SHGD); Algorithm 2 Loopless Stochastic Variance Reduced Hamiltonian Gradient (L-SVRHG); Algorithm 3 L-SVRHG (with Restart) |
| Open Source Code | No | The paper does not contain any explicit statements or links indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | In Gaussian GAN, we have a dataset of real data xreal and latent variable z from a normal distribution with mean 0 and standard deviation 1. (Section 7.2). For Bilinear Games: we choose n = d1 = d2 = 100, [Ai]kl = 1 if i = k = l and 0 otherwise, and [bi]k, [ci]k N(0, 1/n). (Section 7.1) |
| Dataset Splits | No | The paper does not specify training, validation, or test dataset splits (e.g., percentages or sample counts) for reproducibility. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU/CPU models, memory) used to run the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies (e.g., library names with version numbers) needed to replicate the experiments. |
| Experiment Setup | Yes | We provide further details about the experiments and choice of hyperparameters for the different methods in Appendix F. (Section 7) |