3 papers
cs.LG2025
Toward Scalable and Valid Conditional Independence Testing with Spectral Representations
Alek Fröhlich, Vladimir R. Kostic, Karim Lounici +3
Conditional independence (CI) is central to causal inference, feature selection, and graphical modeling, yet it is untestable in many settings without additional assumptions. Exist…
cs.LG2025
Representation Learning for Equivariant Inference with Guarantees
Daniel Ordoñez-Apraez, Vladimir Kostić, Alek Fröhlich +3
In many real-world applications of regression, conditional probability estimation, and uncertainty quantification, exploiting symmetries rooted in physics or geometry can dramatica…
cs.LG2024
PersonalizedUS: Interpretable Breast Cancer Risk Assessment with Local Coverage Uncertainty Quantification
Alek Fröhlich, Thiago Ramos, Gustavo Cabello +3
Correctly assessing the malignancy of breast lesions identified during ultrasound examinations is crucial for effective clinical decision-making. However, the current "golden stand…