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ORCIDYehonatan Elisha
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2020 – today
- 2026
[j1]Liya Gurevitch
, Veronika Bogina, Oren Barkan, Yahlly Schein, Yehonatan Elisha, Noam Koenigstein:
LXR: Learning to eXplain Recommendations. Trans. Recomm. Syst. 4(2): 22:1-22:39 (2026)- [c15]Yehonatan Elisha, Seffi Cohen, Oren Barkan, Noam Koenigstein:
Rethinking Saliency Maps: A Cognitive Human Aligned Taxonomy and Evaluation Framework for Explanations. AAAI 2026: 3750-3758 - [c14]Dor Arviv, Yehonatan Elisha, Oren Barkan, Noam Koenigstein:
Extracting Interaction-Aware Monosemantic Concepts in Recommender Systems. AAAI 2026: 14450-14458 - [c13]Oren Barkan, Yahlly Schein, Yehonatan Elisha, Veronika Bogina, Mikhail Baklanov, Noam Koenigstein:
Fidelity-Aware Recommendation Explanations via Stochastic Path Integration. AAAI 2026: 14484-14492 - [i9]Yehonatan Elisha, Oren Barkan, Noam Koenigstein:
Concept-Guided Fine-Tuning: Steering ViTs away from Spurious Correlations to Improve Robustness. CoRR abs/2603.08309 (2026) - 2025
- [c12]Oren Barkan, Yehonatan Elisha, Jonathan Weill, Noam Koenigstein:
BEE: Metric-Adapted Explanations via Baseline Exploration-Exploitation. AAAI 2025: 1835-1843 - [c11]Nurit Cohen-Inger, Yehonatan Elisha, Bracha Shapira, Lior Rokach, Seffi Cohen:
Forget What You Know about LLMs Evaluations - LLMs are Like a Chameleon. EMNLP 2025: 21664-21677 - [c10]Ziv Weiss Haddad, Oren Barkan, Yehonatan Elisha, Noam Koenigstein:
Soft Local Completeness: Rethinking Completeness in XAI. ICCV 2025: 19794-19804 - [c9]Mikhail Baklanov, Veronika Bogina, Yehonatan Elisha, Yahlly Schein, Liron I. Allerhand, Oren Barkan, Noam Koenigstein:
Refining Fidelity Metrics for Explainable Recommendations. SIGIR 2025: 2967-2971 - [i8]Nurit Cohen-Inger, Yehonatan Elisha, Bracha Shapira, Lior Rokach, Seffi Cohen:
Forget What You Know about LLMs Evaluations - LLMs are Like a Chameleon. CoRR abs/2502.07445 (2025) - [i7]Yehonatan Elisha, Seffi Cohen, Oren Barkan, Noam Koenigstein:
Rethinking Saliency Maps: A Cognitive Human Aligned Taxonomy and Evaluation Framework for Explanations. CoRR abs/2511.13081 (2025) - [i6]Dor Arviv, Yehonatan Elisha, Oren Barkan, Noam Koenigstein:
Extracting Interaction-Aware Monosemantic Concepts in Recommender Systems. CoRR abs/2511.18024 (2025) - [i5]Oren Barkan, Yahlly Schein, Yehonatan Elisha, Veronika Bogina, Mikhail Baklanov, Noam Koenigstein:
Fidelity-Aware Recommendation Explanations via Stochastic Path Integration. CoRR abs/2511.18047 (2025) - 2024
- [c8]Oren Barkan, Yonatan Toib, Yehonatan Elisha, Noam Koenigstein:
A Learning-based Approach for Explaining Language Models. CIKM 2024: 98-108 - [c7]Yehonatan Elisha, Oren Barkan, Noam Koenigstein:
Probabilistic Path Integration with Mixture of Baseline Distributions. CIKM 2024: 570-580 - [c6]Oren Barkan, Yehonatan Elisha, Yonatan Toib, Jonathan Weill, Noam Koenigstein:
Improving LLM Attributions with Randomized Path-Integration. EMNLP (Findings) 2024: 9430-9446 - [c5]Oren Barkan, Yonatan Toib, Yehonatan Elisha, Jonathan Weill, Noam Koenigstein:
LLM Explainability via Attributive Masking Learning. EMNLP (Findings) 2024: 9522-9537 - [i4]Oren Barkan, Yehonatan Elisha, Jonathan Weill, Noam Koenigstein:
BEE: Metric-Adapted Explanations via Baseline Exploration-Exploitation. CoRR abs/2412.17512 (2024) - 2023
- [c4]Oren Barkan, Yehonatan Elisha, Jonathan Weill, Yuval Asher, Amit Eshel, Noam Koenigstein:
Deep Integrated Explanations. CIKM 2023: 57-67 - [c3]Oren Barkan, Yehonatan Elisha, Yuval Asher, Amit Eshel, Noam Koenigstein:
Visual Explanations via Iterated Integrated Attributions. ICCV 2023: 2073-2084 - [c2]Oren Barkan, Yehonatan Elisha, Jonathan Weill, Yuval Asher, Amit Eshel, Noam Koenigstein:
Stochastic Integrated Explanations for Vision Models. ICDM 2023: 938-943 - [c1]Oren Barkan, Yuval Asher, Amit Eshel, Yehonatan Elisha, Noam Koenigstein:
Learning to Explain: A Model-Agnostic Framework for Explaining Black Box Models. ICDM 2023: 944-949 - [i3]Oren Barkan, Yehonatan Elisha, Jonathan Weill, Yuval Asher, Amit Eshel, Noam Koenigstein:
Deep Integrated Explanations. CoRR abs/2310.15368 (2023) - [i2]Oren Barkan, Yuval Asher, Amit Eshel, Yehonatan Elisha, Noam Koenigstein:
Learning to Explain: A Model-Agnostic Framework for Explaining Black Box Models. CoRR abs/2310.16584 (2023) - [i1]Oren Barkan, Yehonatan Elisha, Yuval Asher, Amit Eshel, Noam Koenigstein:
Visual Explanations via Iterated Integrated Attributions. CoRR abs/2310.18585 (2023)
Coauthor Index
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