One-Shot Federated Conformal Prediction
Authors: Pierre Humbert, Batiste Le Bars, Aurélien Bellet, Sylvain Arlot
ICML 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we evaluate Fed CP-QQ on synthetic and real regression data sets. Additional experiments on unbalanced data sets and on Fed CP2-QQ are presented in Appendices A.1 and B.2. |
| Researcher Affiliation | Academia | 1Universit e Paris-Saclay, CNRS, Inria, Laboratoire de math ematiques d Orsay, 91405, Orsay, France. 2Universit e Lille, Inria, CNRS, Centrale Lille, UMR 9189, CRISt AL, F-59000 Lille. |
| Pseudocode | Yes | Algorithm 1 Fed CP-QQ |
| Open Source Code | Yes | The code of our two methods is available at https://github.com/pierre Hmbt/Fed CP-QQ. |
| Open Datasets | Yes | We evaluate our method on five public-domain regression data sets also considered by Romano et al. (2019) and Sesia & Romano (2021): physicochemical properties of protein tertiary structure (bio) (Rana, 2013); bike sharing (bike) (Fanaee-T & Gama, 2013); communities and crimes (community) (Redmond, 2011); Tennessee s student teacher achievement ratio (star) (Achilles et al., 2008); and concrete compressive strength (concrete) (Yeh, 1998). |
| Dataset Splits | Yes | For each experiment, we split the full data set into three parts: a training set (40%), a calibration set (40%), and a test set (20%). |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper mentions software components like 'quantile regression forests' and 'Neural Networks for quantile regression', but does not provide specific version numbers for software dependencies or libraries. |
| Experiment Setup | Yes | The number of trees in the forest is set to 1000, the two parameters controlling the coverage rate on the training data are tuned using cross-validation and the remaining hyperparameters are set as done by Romano et al. (2019). ... CQR with Neural Networks (NN) for quantile regression (Taylor, 2000) the architecture and the parameters are those used by Romano et al. (2019). |