4 papers
Learning Conditional Expectation Operators via Functional Newton Updates
Thiago Ramos, Alek Fröhlich, Daniel Perazzo +1
We introduce the Functional Spectral-Newton Method (FSNM) for learning the leading singular structure of a conditional expectation operator without fixing a basis or reproducing ke…
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…
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…
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…