Generalized Fast Exact Conformalization
Authors: Diyang Li
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We provide experimental results on real-world benchmarks to validate our derived algorithm.The significant speedups of our algorithm as compared to the existing standard methods are demonstrated across numerous benchmarks. |
| Researcher Affiliation | Academia | Diyang Li Cornell University diyang01@cs.cornell.edu |
| Pseudocode | Yes | Algorithm 1 Fast Exact Conformalization Algorithm |
| Open Source Code | No | This paper does not release new assets. |
| Open Datasets | Yes | Our experiments were conducted using real-world datasets. We employ real-world datasets from Open ML [29] and UCI repository [30] in simulations. |
| Dataset Splits | Yes | We randomly partition the dataset into training set, testing set, and calibration set (used in SCP) with 70%, 10%, and 20% of the total samples. |
| Hardware Specification | Yes | All experiments presented in this study were conducted on a workstation running the Ubuntu 18.04 operating system, equipped with Intel Xeon Gold 5218R CPU 64 and 60.9 GB of RAM. |
| Software Dependencies | No | We integrate a system of ordinary differential equations using lsoda from the FORTRAN library, where an interface for Sci Py is available using the odepack. |
| Experiment Setup | Yes | The concrete parameter settings of ODE solver are shown in the Table 3, wherein the numerical solver exploit the Runge-Kutta method of order 4 or 5.We use the conformity score function Ai = |yi ηw (xi)|. |