@misc{meunier2025outcomeawarespectralfeaturelearning,title={Outcome-Aware Spectral Feature Learning for Instrumental Variable Regression},author={Meunier, Dimitri and Wornbard, Jakub and Kostic, Vladimir R. and Moulin, Antoine and Fröhlich, Alek and Lounici, Karim and Pontil, Massimiliano and Gretton, Arthur},year={2025},eprint={2512.00919},archiveprefix={arXiv},primaryclass={stat.ML},}
Equivariant Representation Learning for Symmetry-Aware Inference with Guarantees
Daniel Ordoñez-Apraez, Vladimir Kostić, Alek Fröhlich, and 3 more authors
@misc{ordoñezapraez2025equivariantrepresentationlearningsymmetryaware,title={Equivariant Representation Learning for Symmetry-Aware Inference with Guarantees},author={Ordoñez-Apraez, Daniel and Kostić, Vladimir and Fröhlich, Alek and Brandt, Vivien and Lounici, Karim and Pontil, Massimiliano},year={2025},eprint={2505.19809},archiveprefix={arXiv},primaryclass={cs.LG},}
PersonalizedUS: Interpretable Breast Cancer Risk Assessment with Local Coverage Uncertainty Quantification
Alek Fröhlich, Thiago Ramos, Gustavo Motta Cabello Dos Santos, and 3 more authors
Proceedings of the AAAI Conference on Artificial Intelligence, Apr 2025
@article{Frohlich2025,title={PersonalizedUS: {I}nterpretable Breast Cancer Risk Assessment with Local Coverage Uncertainty Quantification},volume={39},doi={10.1609/aaai.v39i27.35017},number={27},journal={Proceedings of the AAAI Conference on Artificial Intelligence},author={Fröhlich, Alek and Ramos, Thiago and Santos, Gustavo Motta Cabello Dos and Buzatto, Isabela Panzeri Carlotti and Izbicki, Rafael and Tiezzi, Daniel Guimarães},year={2025},month=apr,pages={27998-28006},}
2024
Machine learning can reliably predict malignancy of breast lesions based on clinical and ultrasonographic features
Isabela Carlotti Buzatto, Sarah Abud Recife, Licerio Miguel, and 7 more authors
@article{Buzatto2024,title={Machine learning can reliably predict malignancy of breast lesions based on clinical and ultrasonographic features},author={Buzatto, Isabela Carlotti and Recife, Sarah Abud and Miguel, Licerio and Onari, Nilton and Faim, Ana Luiza Peloso and Bonini, Ruth Morais and Silvestre, Liliane and Carlotti, Danilo Panzeri and Fr\"{o}hlich, Alek and Tiezzi, Daniel Guimar{\~{a}}es},journal={Breast Cancer Research and Treatment},year={2024},month=jul,publisher={Springer Science and Business Media LLC},}
Elements of learning theory and their application in the prediction of malignancy of breast lesions
@inproceedings{Buono2024,title={Molecular/genomic profile enhances prediction of response to target therapy in HER2-positive breast cancer},author={Tiezzi, Daniel and Buono, Fabiana and Fröhlich, Alek and Pagnotta, Stefano},booktitle={ESMO Open},organization={ESMO},year={2024},month=feb,}
2023
Computational pathology pipeline enables quantification of intratumor heterogeneity and tumor-infiltrating lymphocyte score
Daniel Tiezzi, Alek Fröhlich, Stefano Pagnotta, and 1 more author
@inproceedings{Alek2023,title={Computational pathology pipeline enables quantification of intratumor heterogeneity and tumor-infiltrating lymphocyte score},author={Tiezzi, Daniel and Fröhlich, Alek and Pagnotta, Stefano and Chahud, Fernando},booktitle={ESMO Immuno-Oncology Congress},organization={ESMO},year={2023},month=dec,}
2022
Foundations of machine learning: functional analysis with an eye to kernel methods
