PAC-Bayesian AUC classification and scoring
Authors: James Ridgway, Pierre Alquier, Nicolas Chopin, Feng Liang
NeurIPS 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We now compare our PAC-Bayesian approach (computed with EP) with Bayesian logistic regression (to deal with non-identifiable cases), and with the rankboost algorithm [Freund et al., 2003] on different datasets1; note that Cortes and Mohri [2003] showed that the function optimised by rankbook is AUC. and Table 1: Comparison of AUC. |
| Researcher Affiliation | Academia | James Ridgway CREST and CEREMADE University Dauphine james.ridgway@ensae.fr Pierre Alquier CREST (ENSAE) pierre.alquier@ucd.ie Nicolas Chopin CREST (ENSAE) and HEC Paris nicolas.chopin@ensae.fr Feng Liang University of Illinois at Urbana-Champaign liangf@illinois.edu |
| Pseudocode | Yes | Algorithm 1 Tempering SMC |
| Open Source Code | No | No explicit statement about providing open-source code for the methodology described in this paper. |
| Open Datasets | Yes | All available at http://archive.ics.uci.edu/ml/ |
| Dataset Splits | Yes | As mentioned in Section 3, we set the prior hyperparameters by maximizing the evidence, and we use cross-validation to choose γ. |
| Hardware Specification | No | No mention of specific hardware used for experiments. |
| Software Dependencies | No | No specific software dependencies with version numbers are provided. |
| Experiment Setup | Yes | As mentioned in Section 3, we set the prior hyperparameters by maximizing the evidence, and we use cross-validation to choose γ. To ensure convergence of EP, when dealing with difficult sites, we use damping [Seeger, 2005]. |