{q}unfold: Composable Quantification and Unfolding Methods in {P}ython
We present qunfold, a Python package that implements several quantification and unfolding methods. A unique capability of qunfold is the composition of novel methods from existing and easily customized loss functions and data representations. Moreover, this package leverages a powerful optimization back-end to yield state-of-the-art performances for all compositions. This paper introduces the common usage patterns for qunfold, revisits the technical background of the package, and empirically demonstrates the resulting performance.
- Published in:
International Workshop on Learning to Quantify - Type:
Inproceedings - Authors:
Bunse, Mirko - Year:
2023 - Source:
https://lq-2023.github.io/proceedings/CompleteVolume.pdf
Citation information
Bunse, Mirko: {q}unfold: Composable Quantification and Unfolding Methods in {P}ython, International Workshop on Learning to Quantify, 2023, 1--7, September, https://lq-2023.github.io/proceedings/CompleteVolume.pdf, Bunse.2023a,
@Inproceedings{Bunse.2023a,
author={Bunse, Mirko},
title={{q}unfold: Composable Quantification and Unfolding Methods in {P}ython},
booktitle={International Workshop on Learning to Quantify},
pages={1--7},
month={September},
url={https://lq-2023.github.io/proceedings/CompleteVolume.pdf},
year={2023},
abstract={We present qunfold, a Python package that implements several quantification and unfolding methods. A unique capability of qunfold is the composition of novel methods from existing and easily customized loss functions and data representations. Moreover, this package leverages a powerful optimization back-end to yield state-of-the-art performances for all compositions. This paper introduces the...}}