Stochastic Hamiltonian Gradient Methods for Smooth Games
Authors: Nicolas Loizou, Hugo Berard, Alexia Jolicoeur-Martineau, Pascal Vincent, Simon Lacoste-Julien, Ioannis Mitliagkas
ICML 2020 | Conference PDF | Archive PDF | Plain Text | 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 <loizouni@mila.quebec>. |
| 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) |