Learning with Fitzpatrick Losses
Authors: Seta Rakotomandimby, Jean-Philippe Chancelier, Michel De Lara, Mathieu Blondel
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | 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 seta.rakotomandimby@enpc.fr Jean-Philippe Chancelier Ecole des Ponts jean-philippe.chancelier@enpc.fr Michel De Lara Ecole des Ponts michel.delara@enpc.fr Mathieu Blondel Google Deep Mind mblondel@google.com |
| 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. |