Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
The Missing Invariance Principle found -- the Reciprocal Twin of Invariant Risk Minimization
Authors: Dongsung Huh, Avinash Baidya
NeurIPS 2022 | Venue PDF | LLM Run Details | Input Tokens: 17,712 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,674 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We also empirically demonstrate that MRI-v1 strongly out-performs IRM-v1 and consistently achieves near-optimal OOD generalization in image-based nonlinear problems. |
| Researcher Affiliation | Collaboration | Dongsung Huh MIT-IBM Watson AI Lab Cambridge, MA 02142 EMAIL Avinash Baidya Department of Physics and Astronomy University of California Davis, CA 95616 EMAIL |
| Pseudocode | No | The paper does not contain a clearly labeled 'Pseudocode' or 'Algorithm' block. |
| Open Source Code | Yes | Code available at https://github.com/IBM/MRI. |
| Open Datasets | Yes | Colored MNIST (CMNIST) CMNIST (Arjovsky et al., 2019) is a synthetic dataset derived from MNIST for binary classification. |
| Dataset Splits | No | The paper mentions 'two training environments Etr = {e1, e2}' and 'testing environment Etest = {e0}' but does not explicitly provide specific training/validation/test dataset splits (e.g., percentages or sample counts) within these environments in the main text. |
| Hardware Specification | No | The paper states that compute resources are detailed in the Supplementary Materials but does not provide specific hardware details (e.g., GPU/CPU models, memory) in the main text. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., 'PyTorch 1.9', 'Python 3.8') in the main text. |
| Experiment Setup | No | The paper mentions testing algorithms 'under a wide range of hyperparameters' and refers to 'Supplementary Materials' for 'training details (e.g., data splits, hyperparameters, how they were chosen)', but does not provide concrete hyperparameter values or detailed experimental setup in the main text. |