RIO Server Software Copyright (C) 2020 Cognizant Digital Business, Evolutionary AI. All Rights Reserved.
Code and supporting materials for the ICLR 2020 RIO paper
This repository contains all the source codes to reproduce the experimental results reported in paper "Quantifying Point-Prediction Uncertainty in Neural Networks via Residual Estimation with an I/O Kernel", which is published in ICLR 2020. (Arxiv Link: https://arxiv.org/abs/1906.00588)
Before running the codes, three directories need to be created under the current path:
./Datasets/ - contains the original datasets downloaded from UCI Machine Learning Repository (https://archive.ics.uci.edu/ml/datasets.php?format=&task=reg&att=&area=&numAtt=&numIns=&type=&sort=nameUp&view=table)
./Plots/ - for storing generated figures
./Results/ - for storing experimental results
main_experiments_RIO_variants.py - main file to run tests for all the RIO variants on all the datasets
util.py - contains functions to read data and run RIO variants
Results_Table1.py - file for post-processing all the experimental results for Table 1 (in the main paper)
Results_Figure_RMSE.py - file for plotting all the figures in Figure 3
Results_Figure_CI.py - file for plotting all the figures in Figure 4 and Figure 5
Results_Spearman_correlation.py - file for calculating Spearman's rank correlation between RMSE and noise variance
If you use RIO in your research, please cite it using the following BibTeX entry
@inproceedings{qiu:iclr20,
title={Quantifying Point-Prediction Uncertainty in Neural Networks via Residual Estimation with an I/O Kernel},
author={Xin Qiu and Elliot Meyerson and Risto Miikkulainen},
booktitle={International Conference on Learning Representations},
year={2020}
}