2024
Yang, Fan; Bodic, Pierre Le; Kamp, Michael; Boley, Mario
Orthogonal Gradient Boosting for Interpretable Additive Rule Ensembles Proceedings Article
In: Proceedings of the 26th International Conference on Artificial Intelligence and Statistics (AISTATS), 2024.
Abstract | BibTeX | Tags: complexity, explainability, interpretability, interpretable, machine learning, rule ensemble, rule mining, XAI
@inproceedings{yang2024orthogonal,
title = {Orthogonal Gradient Boosting for Interpretable Additive Rule Ensembles},
author = {Fan Yang and Pierre Le Bodic and Michael Kamp and Mario Boley},
year = {2024},
date = {2024-05-02},
urldate = {2024-05-02},
booktitle = {Proceedings of the 26th International Conference on Artificial Intelligence and Statistics (AISTATS)},
abstract = {Gradient boosting of prediction rules is an efficient approach to learn potentially interpretable yet accurate probabilistic models. However, actual interpretability requires to limit the number and size of the generated rules, and existing boosting variants are not designed for this purpose. Though corrective boosting refits all rule weights in each iteration to minimise prediction risk, the included rule conditions tend to be sub-optimal, because commonly used objective functions fail to anticipate this refitting. Here, we address this issue by a new objective function that measures the angle between the risk gradient vector and the projection of the condition output vector onto the orthogonal complement of the already selected conditions. This approach correctly approximates the ideal update of adding the risk gradient itself to the model and favours the inclusion of more general and thus shorter rules. As we demonstrate using a wide range of prediction tasks, this significantly improves the comprehensibility/accuracy trade-off of the fitted ensemble. Additionally, we show how objective values for related rule conditions can be computed incrementally to avoid any substantial computational overhead of the new method.},
keywords = {complexity, explainability, interpretability, interpretable, machine learning, rule ensemble, rule mining, XAI},
pubstate = {published},
tppubtype = {inproceedings}
}
Gradient boosting of prediction rules is an efficient approach to learn potentially interpretable yet accurate probabilistic models. However, actual interpretability requires to limit the number and size of the generated rules, and existing boosting variants are not designed for this purpose. Though corrective boosting refits all rule weights in each iteration to minimise prediction risk, the included rule conditions tend to be sub-optimal, because commonly used objective functions fail to anticipate this refitting. Here, we address this issue by a new objective function that measures the angle between the risk gradient vector and the projection of the condition output vector onto the orthogonal complement of the already selected conditions. This approach correctly approximates the ideal update of adding the risk gradient itself to the model and favours the inclusion of more general and thus shorter rules. As we demonstrate using a wide range of prediction tasks, this significantly improves the comprehensibility/accuracy trade-off of the fitted ensemble. Additionally, we show how objective values for related rule conditions can be computed incrementally to avoid any substantial computational overhead of the new method.
2023
Michael Kamp Linara Adilova, Gennady Andrienko
Re-interpreting Rules Interpretability Journal Article
In: International Journal of Data Science and Analytics, 2023.
BibTeX | Tags: interpretable, machine learning, rule learning, XAI
@article{adilova2023reinterpreting,
title = {Re-interpreting Rules Interpretability},
author = {Linara Adilova, Michael Kamp, Gennady Andrienko, Natalia Andrienko},
year = {2023},
date = {2023-06-30},
urldate = {2023-06-30},
journal = {International Journal of Data Science and Analytics},
keywords = {interpretable, machine learning, rule learning, XAI},
pubstate = {published},
tppubtype = {article}
}
David Kaltenpoth Osman Mian, Michael Kamp
Nothing but Regrets - Privacy-Preserving Federated Causal Discovery Proceedings Article
In: International Conference on Artificial Intelligence and Statistics (AISTATS), 2023.
BibTeX | Tags: causal discovery, causality, explainable, federated, federated causal discovery, federated learning, interpretable
@inproceedings{mian2022nothing,
title = {Nothing but Regrets - Privacy-Preserving Federated Causal Discovery},
author = {Osman Mian, David Kaltenpoth, Michael Kamp, Jilles Vreeken},
year = {2023},
date = {2023-04-25},
urldate = {2023-04-25},
booktitle = {International Conference on Artificial Intelligence and Statistics (AISTATS)},
keywords = {causal discovery, causality, explainable, federated, federated causal discovery, federated learning, interpretable},
pubstate = {published},
tppubtype = {inproceedings}
}
