Optimistic mirror descent in saddle-point problems: Going the extra (gradient) mile
Authors: Panayotis Mertikopoulos, Bruno Lecouat, Houssam Zenati, Chuan-Sheng Foo, Vijay Chandrasekhar, Georgios Piliouras
ICLR 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We also validate our analysis by numerical experiments in a wide array of GAN models (including Gaussian mixture models, and the Celeb A and CIFAR-10 datasets). |
| Researcher Affiliation | Academia | Panayotis Mertikopoulos Univ. Grenoble Alpes, CNRS, Inria, Grenoble INP, LIG 38000 Grenoble, France panayotis.mertikopoulos@imag.fr Bruno Lecouat, Houssam Zenati, Chuan-Sheng Foo, Vijay Chandrasekhar Institute for Infocomm Research, A*STAR 1 Fusionopolis Way, #21-01 Connexis (South Tower), Singapore {bruno_lecouat,houssam_zenati,foocs,vijay}@i2r.a-star.edu.sg Georgios Piliouras Singapore University of Technology and Design 8 Somapah Road, Singapore georgios@sutd.edu.sg |
| Pseudocode | Yes | Algorithm 1: mirror descent (MD) for saddle-point problems; Algorithm 2: optimistic mirror descent (OMD) for saddle-point problems; Algorithm 3: Adam with extra-gradient add-on (optimistic Adam) |
| Open Source Code | No | The paper does not provide a concrete access to source code (specific repository link, explicit code release statement, or code in supplementary materials) for the methodology described. |
| Open Datasets | Yes | We also validate our analysis by numerical experiments in a wide array of GAN models (including Gaussian mixture models, and the Celeb A and CIFAR-10 datasets). |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) needed to reproduce the data partitioning into train/validation/test sets. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions training with RMSprop and Adam, but it does not provide specific ancillary software details, such as library names with version numbers, needed to replicate the experiment. |
| Experiment Setup | Yes | Table 2: Image experiments settings batch size = 64 Adam learning rate = 0.0001 Adam β1 = 0.0 Adam β2 = 0.9 max iterations = 200000 WGAN-GP λ = 1.0 WGAN-GP ndis = 1 GAN objective = WGAN-GP Optimizer = extra-Adam or Adam |