Estimating weighted areas under the ROC curve
Authors: Andreas Maurer, Massimiliano Pontil
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Exponential bounds on the estimation error are given for the plug-in estimator of weighted areas under the ROC curve. The bounds hold for single score functions and uniformly over classes of functions, whose complexity can be controlled by Gaussian or Rademacher averages. The results justify learning algorithms which select score functions to maximize the empirical partial area under the curve (p AUC). They also illustrate the use of some recent advances in the theory of nonlinear empirical processes. |
| Researcher Affiliation | Academia | Andreas Maurer Istituto Italiano di Tecnologia am@andreas-maurer.eu Massimiliano Pontil Istituto Italiano di Tecnologia & University College London massimiliano.pontil@iit.it |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | No | The paper is theoretical and does not conduct experiments on a specific dataset that would require public access information. |
| Dataset Splits | No | The paper is theoretical and does not discuss experimental dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not report on specific hardware used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not mention specific ancillary software details or version numbers required for replication. |
| Experiment Setup | No | The paper is theoretical and does not detail experimental setup specifics such as hyperparameters or training configurations. |