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..
Out-of-Distribution Optimality of Invariant Risk Minimization
Authors: Shoji Toyota, Kenji Fukumizu
TMLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Aiming at providing a theoretical justification for IRM, this paper rigorously proves that a solution to the bi-leveled optimization problem (3) also minimizes the o.o.d. risk (1) under certain conditions. The result also provides sufficient conditions on distributions providing training data and on a dimension of a feature space for the bi-leveled optimization problem to minimize the o.o.d. risk. |
| Researcher Affiliation | Academia | Shoji Toyota EMAIL The Institute of Statistical Mathematics Kenji Fukumizu EMAIL The Institute of Statistical Mathematics |
| Pseudocode | No | The paper focuses on theoretical proofs and mathematical formulations without providing any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any statements regarding the release of source code or links to a code repository. |
| Open Datasets | No | The paper focuses on theoretical analysis of distributions and data properties, but does not use specific experimental datasets or provide access information for any publicly available datasets. |
| Dataset Splits | No | The paper does not describe any experiments that would require dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not describe any experimental setup or hardware used. |
| Software Dependencies | No | The paper is theoretical and does not mention any specific software dependencies or versions. |
| Experiment Setup | No | The paper is theoretical and does not describe any experimental setup or specific hyperparameters. |