Statistical Learning
Nearly-Tight and Oblivious Algorithms for Explainable Clustering
The algorithm is remarkably simple.
Differentially Private Empirical Risk Minimization under the Fairness Lens
It allows to measure and bound the risk associated with an individual participation in a computation.
Robust Calibration with Multi-domain Temperature Scaling
A significant challenge to accurately codifying uncertainty is the distribution shift that typically arises over the course of a system's deployment [Quiñonero-Candela et al., 2008].
Robust Calibration with Multi-domain Temperature Scaling
A significant challenge to accurately codifying uncertainty is the distribution shift that typically arises over the course of a system's deployment [ Quiñonero-Candela et al., 2008 ].