On Volume Minimization in Conformal Regression
Authors: Batiste Le Bars, Pierre Humbert
ICML 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we evaluate the empirical performance and the robustness of our methodologies. In Section 5, a set of synthetic data experiments illustrates the empirical performance and the robustness of our approaches on asymmetric and heavytailed distributions. |
| Researcher Affiliation | Academia | 1Univ. Lille, Inria, CNRS, Centrale Lille, UMR 9189, CRISt AL, F-59000 Lille 2Sorbonne Université et Univ. Paris Cité, CNRS, LPSM, F-75005 Paris, France. |
| Pseudocode | Yes | Algorithm 1 Gradient descent to solve the QAE problem (step 1 of Eff Ort and Ad-Eff Ort) |
| Open Source Code | Yes | Code to run all methods is available at https: //github.com/pierre Hmbt/Ad Eff Ort. |
| Open Datasets | Yes | We finally compare Ad-Eff Ort with Locally Weighted CP (LW-CP) and CQR on the following public-domain real data sets also considered in e.g. (Romano et al., 2019): abalone (Nash et al., 1994), boston housing (housing) (Harrison Jr & Rubinfeld, 1978)2, and concrete compressive strength (concrete) (Yeh, 1998). |
| Dataset Splits | Yes | For each scenario, we generate nlrn = ncal = 1000 pairs (Xi, Yi), as well as ntest = 1000 test points to compute the empirical marginal coverage and the average size of the returned set. We randomly split each data set 10 times into a training set, a calibration set and a test set of respective "size" 40%, 40%, and 20%. |
| Hardware Specification | No | No specific hardware details (GPU/CPU models, memory, etc.) were found in the paper regarding the execution of experiments. The paper focuses on software implementations and experimental setup parameters without specifying the underlying hardware. |
| Software Dependencies | No | The function ˆs( ) (second step of Ad-Eff Ort) and the two quantile regression functions of CQR are learned by using a Random Forest (RF) quantile regressor, implemented in the Python package sklearn-quantile1. The function ˆσ in LW-CP is learned using the RF regression implementation of scikit-learn (Pedregosa et al., 2011). |
| Experiment Setup | Yes | The smoothing parameter ε is set to 0.1, niter = 1000, and the step-size sequence is {(1/t)0.6}niter t=1 . using a robust linear regression with Huber loss with parameter δ = 1.35. we set α = 0.1. the max-depth of the RF is set to 5 |