Statistical Learning
Tikhonov Regularization is Optimal Transport Robust under Martingale Constraints
To realize these novel tools, tractable computational algorithms are proposed.
An Axiomatic Theory of Provably-Fair Welfare-Centric Machine Learning
We avoid this issue by working directly with malfare and loss.
TOIST: Task Oriented Instance Segmentation Transformer with Noun-Pronoun Distillation
Noun prototypes are generated in an unsupervised manner and contextual pronoun features are trained to select prototypes. As such, the network remains noun-agnostic during inference.
A Fair Classifier Using Kernel Density Estimation
It is now employed to make critical decisions that affect our lives, cultures, and rights, e.g., screening job applicants, and informing bail & parole decisions.
