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..
Exact Generalization Guarantees for (Regularized) Wasserstein Distributionally Robust Models
Authors: Waïss Azizian, Franck Iutzeler, Jérôme Malick
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In Section H, we also provide numerical illustrations for linear models. |
| Researcher Affiliation | Academia | Waïss Azizian, Franck Iutzeler, Jérôme Malick Univ. Grenoble Alpes, CNRS, Grenoble INP Grenoble, 38000, France EMAIL |
| Pseudocode | No | The paper does not contain any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access information (e.g., repository link, explicit statement of code release) for source code. |
| Open Datasets | No | While the paper mentions "numerical illustrations" in the appendix, it does not provide concrete access information (link, DOI, citation with authors/year) for any publicly available or open dataset in the provided text. |
| Dataset Splits | No | The paper does not provide specific dataset split information (e.g., percentages, sample counts) for training, validation, or testing. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, 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). |
| Experiment Setup | No | The paper does not contain specific experimental setup details (e.g., concrete hyperparameter values, training configurations) in the main text. |