5 citations · 8 across the 4 of their papers we have counts for
4 papers
A Unifying Post-Processing Framework for Multi-Objective Learn-to-Defer Problems
Mohammad-Amin Charusaie, Samira Samadi
Learn-to-Defer is a paradigm that enables learning algorithms to work not in isolation but as a team with human experts. In this paradigm, we permit the system to defer a subset of…
Sample Efficient Learning of Predictors that Complement Humans
Mohammad-Amin Charusaie, Hussein Mozannar, David Sontag +1
One of the goals of learning algorithms is to complement and reduce the burden on human decision makers. The expert deferral setting wherein an algorithm can either predict on its…
Hermite Polynomial Features for Private Data Generation
Margarita Vinaroz, Mohammad-Amin Charusaie, Frederik Harder +2
Kernel mean embedding is a useful tool to represent and compare probability measures. Despite its usefulness, kernel mean embedding considers infinite-dimensional features, which a…
Compressibility Measures for Affinely Singular Random Vectors
Mohammad-Amin Charusaie, Arash Amini, Stefano Rini
There are several ways to measure the compressibility of a random measure; they include general approaches such as using the rate-distortion curve, as well as more specific notions…