no code implementations • 30 Nov 2023 • Yuli Slavutsky, Yuval Benjamini
Class distribution shifts are particularly challenging for zero-shot classifiers, which rely on representations learned from training classes but are deployed on new, unseen ones.
1 code implementation • 28 Jul 2023 • Bitya Neuhof, Yuval Benjamini
We propose a novel method for the post-hoc interpretation of feature importance values that is based on the framework and pairwise comparisons of the feature importance values.
no code implementations • 23 Mar 2023 • Guy Ashiri-Prossner, Yuval Benjamini
We develop a cascading set of equivalence tests, in which each test addresses a different aspect of the model: the way the phenomenon is coded in the regression coefficients, the individual predictions in the per example log odds ratio and the overall accuracy in the mean square prediction error.
no code implementations • ICLR 2021 • Yuli Slavutsky, Yuval Benjamini
We show that the classification accuracy is a function of the rROC in multiclass classifiers, for which the learned representation of data from the initial class sample remains unchanged when new classes are added.
1 code implementation • 27 Dec 2017 • Charles Zheng, Rakesh Achanta, Yuval Benjamini
The difficulty of multi-class classification generally increases with the number of classes.
no code implementations • 16 Jun 2016 • Charles Y. Zheng, Yuval Benjamini
Multivariate pattern analyses approaches in neuroimaging are fundamentally concerned with investigating the quantity and type of information processed by various regions of the human brain; typically, estimates of classification accuracy are used to quantify information.
no code implementations • 16 Jun 2016 • Charles Y. Zheng, Rakesh Achanta, Yuval Benjamini
Using data from a subset of the classes, can we predict how well a classifier will scale with an increased number of classes?