Deep Integrated Explanations. - Oren Barkan; Yehonatan Elisha; Jonathan Weill; Yuval Asher; Amit Eshel; Noam Koenigstein (CIKM, 2023)

Visual Explanations via Iterated Integrated Attributions. - Oren Barkan; Yehonatan Elisha; Yuval Asher; Amit Eshel; Noam Koenigstein (ICCV, 2023)

Learning to Explain: A Model-Agnostic Framework for Explaining Black Box Models. - Oren Barkan; Yuval Asher; Amit Eshel; Yehonatan Elisha; Noam Koenigstein (ICDM, 2023)

Stochastic Integrated Explanations for Vision Models. - Oren Barkan; Yehonatan Elisha; Jonathan Weill; Yuval Asher; Amit Eshel; Noam Koenigstein (ICDM, 2023)

A Learning-based Approach for Explaining Language Models. - Oren Barkan; Yonatan Toib; Yehonatan Elisha; Noam Koenigstein (CIKM, 2024)

Probabilistic Path Integration with Mixture of Baseline Distributions. - Yehonatan Elisha; Oren Barkan; Noam Koenigstein (CIKM, 2024)

Improving LLM Attributions with Randomized Path-Integration. - Oren Barkan; Yehonatan Elisha; Yonatan Toib; Jonathan Weill; Noam Koenigstein (EMNLP (Findings), 2024)

LLM Explainability via Attributive Masking Learning. - Oren Barkan; Yonatan Toib; Yehonatan Elisha; Jonathan Weill; Noam Koenigstein (EMNLP (Findings), 2024)

BEE: Metric-Adapted Explanations via Baseline Exploration-Exploitation. - Oren Barkan; Yehonatan Elisha; Jonathan Weill; Noam Koenigstein (AAAI, 2025)

Refining Fidelity Metrics for Explainable Recommendations. - Mikhail Baklanov; Veronika Bogina; Yehonatan Elisha; Yahlly Schein; Liron I. Allerhand; Oren Barkan; Noam Koenigstein (SIGIR, 2025)

Forget What You Know about LLMs Evaluations - LLMs are Like a Chameleon. - Nurit Cohen-Inger; Yehonatan Elisha; Bracha Shapira; Lior Rokach; Seffi Cohen (CoRR, 2025)