alanjeffares/RQR

Relaxed Quantile Regression: Prediction Intervals for Asymmetric Noise

This repository contains the code associated with our ICML 2024 paper where we introduce a new loss to train Deep Learning models for interval prediction. The Relaxed Quantile Regression (RQR) loss directly learning of the intervals without the intermediary steps of estimating quantiles.

Experiments

Setup

Clone this repository and navigate to the root folder.

git clone https://github.com/TPouplin/RQR.git
cd RQR

Using conda, create and activate a new environment.

conda create -n <environment name> pip python
conda activate <environment name>

Then install the repository requirements.

pip install -r requirements.txt

Data

The datasets mentioned in the paper can be found in the data folder src/data/. The datasets have to be unzipped e.g.

unzip data/datasets.zip -d data

Quick Start To quickly get started with the RQR loss objective, check out [quick_start.ipynb](TODO: add link) which contains a simple implementation of the objective in Pytorch and an illustration on known noise distributions (i.e. reproducing Table 2 from the text).

Running Experiments

The hyperparameter fine-tuning can be run for all the datasets and losses proposed in the paper with the following command :

python finetuning_parallel.py

Results and hyperparameters of these experiments are saved in an optuna database results/finetuning/recording.db.

The optimal hyperparameters found can be used to recreate the benchmark presented in the paper with the following command :

python testing_parallel.py

Citation

If you use this code, please cite the associated paper.

@inproceedings{
      pouplin2024relaxed,
      title={Relaxed Quantile Regression: Prediction Intervals for Asymmetric Noise},
      author={Thomas Pouplin and Alan Jeffares and Nabeel Seedat and Mihaela van der Schaar},
      booktitle={Forty-first International Conference on Machine Learning},
      year={2024},
      url={https://openreview.net/forum?id=L8nSGvoyvb}
}