Publications

BitterMatch: recommendation systems for matching molecules with bitter taste receptors

Published in Journal of Cheminformatics, 2022

A few-shot classification algorithm inspired by recommendation systems, applied to bitter taste molecule-biotarget matching.

Margulis E.*, Slavutsky Y.*, Lang, T., Behrens, M., Benjamini, Y., & Niv, M. Y. (2022). "BitterMatch: recommendation systems for matching molecules with bitter taste receptors." Journal of Cheminformatics. 1(3).
* Equally contributing authors.

Predicting classification accuracy when adding new unobserved classes

Published in Proceedings of the International Conference on Learning Representations (ICLR), 2021

In this work we study how a few-shot classifier’s performance over the initial class sample can be used to extrapolate its expected accuracy on a larger, unobserved set of classes.

Slavutsky, Y., & Benjamini, Y. (2021). "Predicting classification accuracy when adding new unobserved classes." International Conference on Learning Representations (ICLR).

Tracking COVID-19 using taste and smell loss Google searches is not a reliable strategy

Published in Scientific Reports, 2020

Due to shortage of COVID-19 tests several groups proposed relying on Google searches of known symptoms in order to track new COVID-19 cases. In this paper we explored this conjecture.

Asseo, K., Fierro, F., Slavutsky, Y., Frasnelli, J., & Niv, M. Y. (2020). "Tracking COVID-19 using taste and smell loss Google searches is not a reliable strategy." Scientific Reports. 10(1), 1-8.

Preprints

Class Distribution Shifts in Zero-Shot Learning: Learning Robust Representations

Published: November 30, 2023

Class distribution shifts are particularly challenging for zero-shot classifiers, as they rely on representations learned from training classes, but are deployed on new, unseen ones. In this paper, we explore a setting where a hidden variable induces a shift in the distribution of classes and introduce an algorithm that combines hierarchical data sampling with out-of-distribution generalization techniques to learn data representations that are robust to such shifts.

Slavutsky, Y.& Benjamini, Y. (2023). "Class Distribution Shifts in Zero-Shot Learning: Learning Robust Representations." ArXiv Preprint.

Yuli Slavutsky