Yehonatan Elisha

According to our database, Yehonatan Elisha authored at least 17 papers between 2023 and 2026.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2026

LXR: Learning to eXplain Recommendations.

Liya Gurevitch, Veronika Bogina, Oren Barkan, Yahlly Schein, Yehonatan Elisha, Noam Koenigstein

Trans. Recomm. Syst., June, 2026

Concept-Guided Fine-Tuning: Steering ViTs away from Spurious Correlations to Improve Robustness.

Yehonatan Elisha, Oren Barkan, Noam Koenigstein

CoRR, March, 2026

Rethinking Saliency Maps: A Cognitive Human Aligned Taxonomy and Evaluation Framework for Explanations.

Yehonatan Elisha, Seffi Cohen, Oren Barkan, Noam Koenigstein

Proceedings of the Fortieth AAAI Conference on Artificial Intelligence, 2026

Fidelity-Aware Recommendation Explanations via Stochastic Path Integration.

Oren Barkan, Yahlly Schein, Yehonatan Elisha, Veronika Bogina, Mikhail Baklanov, Noam Koenigstein

Proceedings of the Fortieth AAAI Conference on Artificial Intelligence, 2026

Extracting Interaction-Aware Monosemantic Concepts in Recommender Systems.

Dor Arviv, Yehonatan Elisha, Oren Barkan, Noam Koenigstein

Proceedings of the Fortieth AAAI Conference on Artificial Intelligence, 2026

2025

Refining Fidelity Metrics for Explainable Recommendations.

Mikhail Baklanov, Veronika Bogina, Yehonatan Elisha, Yahlly Schein, Liron I. Allerhand, Oren Barkan, Noam Koenigstein

Proceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2025

Soft Local Completeness: Rethinking Completeness in XAI.

Ziv Weiss Haddad, Oren Barkan, Yehonatan Elisha, Noam Koenigstein

Proceedings of the IEEE/CVF International Conference on Computer Vision, 2025

Forget What You Know about LLMs Evaluations - LLMs are Like a Chameleon.

Nurit Cohen-Inger, Yehonatan Elisha, Bracha Shapira, Lior Rokach, Seffi Cohen

Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, 2025

BEE: Metric-Adapted Explanations via Baseline Exploration-Exploitation.

Oren Barkan, Yehonatan Elisha, Jonathan Weill, Noam Koenigstein

Proceedings of the Thirty-Ninth AAAI Conference on Artificial Intelligence, 2025

2024

LLM Explainability via Attributive Masking Learning.

Oren Barkan, Yonatan Toib, Yehonatan Elisha, Jonathan Weill, Noam Koenigstein

Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2024, 2024

Improving LLM Attributions with Randomized Path-Integration.

Oren Barkan, Yehonatan Elisha, Yonatan Toib, Jonathan Weill, Noam Koenigstein

Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2024, 2024

Probabilistic Path Integration with Mixture of Baseline Distributions.

Yehonatan Elisha, Oren Barkan, Noam Koenigstein

Proceedings of the 33rd ACM International Conference on Information and Knowledge Management, 2024

A Learning-based Approach for Explaining Language Models.

Oren Barkan, Yonatan Toib, Yehonatan Elisha, Noam Koenigstein

Proceedings of the 33rd ACM International Conference on Information and Knowledge Management, 2024

2023

Stochastic Integrated Explanations for Vision Models.

Oren Barkan, Yehonatan Elisha, Jonathan Weill, Yuval Asher, Amit Eshel, Noam Koenigstein

Proceedings of the IEEE International Conference on Data Mining, 2023

Learning to Explain: A Model-Agnostic Framework for Explaining Black Box Models.

Oren Barkan, Yuval Asher, Amit Eshel, Yehonatan Elisha, Noam Koenigstein

Proceedings of the IEEE International Conference on Data Mining, 2023

Visual Explanations via Iterated Integrated Attributions.

Oren Barkan, Yehonatan Elisha, Yuval Asher, Amit Eshel, Noam Koenigstein

Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023

Deep Integrated Explanations.

Oren Barkan, Yehonatan Elisha, Jonathan Weill, Yuval Asher, Amit Eshel, Noam Koenigstein

Proceedings of the 32nd ACM International Conference on Information and Knowledge Management, 2023