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..
Learning with Fitzpatrick Losses
Authors: Seta Rakotomandimby, Jean-Philippe Chancelier, Michel De Lara, Mathieu Blondel
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the effectiveness of Fitzpatrick losses for label proportion estimation. |
| Researcher Affiliation | Collaboration | Seta Rakotomandimby Ecole des Ponts EMAIL Jean-Philippe Chancelier Ecole des Ponts EMAIL Michel De Lara Ecole des Ponts EMAIL Mathieu Blondel Google Deep Mind EMAIL |
| Pseudocode | No | The paper does not contain any pseudocode or algorithm blocks. |
| Open Source Code | No | The code will be opened upon release. |
| Open Datasets | Yes | The datasets can be downloaded from http://mulan.sourceforge.net/datasets-mlc.html and https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/. |
| Dataset Splits | Yes | In (5), we chose the hyperparameter λ {10 4, 10 3, . . . , 104} against the validation set. |
| Hardware Specification | Yes | Experiments were conducted on a Intel Xeon E5-2667 clocked at 3.30GHz with 192 GB of RAM running on Linux. |
| Software Dependencies | No | Our implementation relies on the Sci Py [29] and scikit-learn [25] libraries. |
| Experiment Setup | Yes | In (5), we chose the hyperparameter λ {10 4, 10 3, . . . , 104} against the validation set. We optimize (5) using the L-BFGS algorithm [20]. The gradient of the Fenchel-Young loss is given in (2), while the gradient of the Fitzpatrick loss is given in Proposition 1, Item 4. |