Invariant Risk Minimization Games
Authors: Kartik Ahuja, Karthikeyan Shanmugam, Kush Varshney, Amit Dhurandhar
ICML 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 5. Experiments Table 1. Colored MNIST: Comparison of methods in terms of train ing, testing accuracy (mean std deviation). |
| Researcher Affiliation | Industry | 1IBM Research, Thomas J. Watson Research Center, York town Heights, NY. Correspondence to: Kartik Ahuja <kar tik.ahuja@ibm.com>. |
| Pseudocode | Yes | Algorithm 1 Best Response Training |
| Open Source Code | Yes | The source-code is available at https://github.com/IBM/IRM-games. |
| Open Datasets | Yes | Colored MNIST dataset. In Arjovsky et al. (2019), the comparisons were done on a colored digits MNIST dataset. We create the same dataset for our experiments. |
| Dataset Splits | No | There are three environments (two training containing 30,000 points each, one test containing 10,000 points) We add noise to the preliminary label (y = 0 if digit is between 0-4 and y = 1 if the digit is between 5 9) by flipping it with 25 percent probability to construct the final labels. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers like Python 3.8, CPLEX 12.4) needed to replicate the experiment. |
| Experiment Setup | No | The paper states: "The details on architectures, hyperparameters, and optimizers used are in the supplement." These details are not provided in the main text of the paper. |