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Relevant Projects

Photo of Yaniv Romano
Assistant Professor
Reproducible and interpretable data-driven feature selection

Design learning and statistical methodologies to effectively identify explanatory features (e.g., genetic variations) truly linked to a phenomenon under study (e.g., disease risk) while rigorously controlling the number of false positives among the reported features.

Certified Robustness of Modern Machine Learning

Develop methodologies that provide provably robust predictions in a challenging setting where the train and test distribution differ, e.g., due to adversarial attacks.

Prediction with confidence

Develop statistical tools that can work in combination with any complex machine learning algorithm (e.g., a deep neural network) to provide a reliable assessment of prediction uncertainty. The tools we invent treat regression, classification, and out-of-distribution detection problems.