Publications

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

Class distribution shifts challenge zero-shot classifiers, which rely on representations from training classes but are tested on unseen ones. We propose a model that assumes an unknown class attribute causes the shift and present an algorithm to learn robust representations where ERM would fail.

Slavutsky, Y., & Benjamini, Y. (2024). "Class Distribution Shifts in Zero-Shot Learning: Learning Robust Representations." Conference on Neural Information Processing Systems (NeuRIPS).

CONTESTS: a Framework for Consistency Testing of Span Probabilities in Language Models

Different estimators of joint probabilities of word spans arise by marginalizing the LLM’s scores from various completion orders. If the predictions are reliable, these estimates should be consistent. In this paper, we develop a statistical framework to test this and apply it to evaluate different LLMs. We show that both Masked Language Models and autoregressive models exhibit inconsistent predictions.

Wagner, E., Slavutsky, Y., & Abend, O. (2024). "CONTESTS: a Framework for Consistency Testing of Span Probabilities in Language Models." The 2024 Conference on Empirical Methods in Natural Language Processing (EMNLP).

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.