A Unified View of FANOVA: A Comprehensive Bayesian Framework for Component Selection and Estimation

Yosra Marnissi, Maxime Leiber
Proceedings of the 41st International Conference on Machine Learning, PMLR 235:34866-34894, 2024.

Abstract

This paper presents a comprehensive Bayesian framework for FANOVA models. We provide guidelines for tuning and practical implementation to improve scalability of learning and prediction. Our model is very flexible and can handle different levels of sparsity across and within decomposition orders, as well as among covariates. This flexibility enables the modeling of complex real-world data while enhancing interpretability. Additionally, it allows our model to unify diverse deterministic and Bayesian non-parametric approaches into a single equation, making comparisons and understanding easier. Notably, our model serves as the Bayesian counterpart of several deterministic methods allowing uncertainty quantification. This general framework unlocks potential for novel model developments that have been previously overlooked, such as the proposed Dirichlet mixing model that addresses limitations of existing models.

Cite this Paper

BibTeX
@InProceedings{pmlr-v235-marnissi24a, title = {A Unified View of {FANOVA}: A Comprehensive {B}ayesian Framework for Component Selection and Estimation}, author = {Marnissi, Yosra and Leiber, Maxime}, booktitle = {Proceedings of the 41st International Conference on Machine Learning}, pages = {34866--34894}, year = {2024}, editor = {Salakhutdinov, Ruslan and Kolter, Zico and Heller, Katherine and Weller, Adrian and Oliver, Nuria and Scarlett, Jonathan and Berkenkamp, Felix}, volume = {235}, series = {Proceedings of Machine Learning Research}, month = {21--27 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v235/main/assets/marnissi24a/marnissi24a.pdf}, url = {https://proceedings.mlr.press/v235/marnissi24a.html}, abstract = {This paper presents a comprehensive Bayesian framework for FANOVA models. We provide guidelines for tuning and practical implementation to improve scalability of learning and prediction. Our model is very flexible and can handle different levels of sparsity across and within decomposition orders, as well as among covariates. This flexibility enables the modeling of complex real-world data while enhancing interpretability. Additionally, it allows our model to unify diverse deterministic and Bayesian non-parametric approaches into a single equation, making comparisons and understanding easier. Notably, our model serves as the Bayesian counterpart of several deterministic methods allowing uncertainty quantification. This general framework unlocks potential for novel model developments that have been previously overlooked, such as the proposed Dirichlet mixing model that addresses limitations of existing models.} }
Endnote
%0 Conference Paper %T A Unified View of FANOVA: A Comprehensive Bayesian Framework for Component Selection and Estimation %A Yosra Marnissi %A Maxime Leiber %B Proceedings of the 41st International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2024 %E Ruslan Salakhutdinov %E Zico Kolter %E Katherine Heller %E Adrian Weller %E Nuria Oliver %E Jonathan Scarlett %E Felix Berkenkamp %F pmlr-v235-marnissi24a %I PMLR %P 34866--34894 %U https://proceedings.mlr.press/v235/marnissi24a.html %V 235 %X This paper presents a comprehensive Bayesian framework for FANOVA models. We provide guidelines for tuning and practical implementation to improve scalability of learning and prediction. Our model is very flexible and can handle different levels of sparsity across and within decomposition orders, as well as among covariates. This flexibility enables the modeling of complex real-world data while enhancing interpretability. Additionally, it allows our model to unify diverse deterministic and Bayesian non-parametric approaches into a single equation, making comparisons and understanding easier. Notably, our model serves as the Bayesian counterpart of several deterministic methods allowing uncertainty quantification. This general framework unlocks potential for novel model developments that have been previously overlooked, such as the proposed Dirichlet mixing model that addresses limitations of existing models.
APA
Marnissi, Y. & Leiber, M.. (2024). A Unified View of FANOVA: A Comprehensive Bayesian Framework for Component Selection and Estimation. Proceedings of the 41st International Conference on Machine Learning, in Proceedings of Machine Learning Research 235:34866-34894 Available from https://proceedings.mlr.press/v235/marnissi24a.html.

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