Exact Generalization Guarantees for (Regularized) Wasserstein Distributionally Robust Models
Authors: Waïss Azizian, Franck Iutzeler, Jérôme Malick
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | 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 firstname.lastname@univ-grenoble-alpes.fr |
| 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. |