Publications
2024
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2023
דלית קן־דרור פלדמן, קרין נהון, ורדית אבידן ורן גלעד בכרך
זיוף, זיוף עמוק (Deep-Fake), חוק הסרטונים ועקרון הפרשנות המצמצמת: מה הדין?
Maya Bechler-Speicher, Ido Amos, Ran Gilad-Bachrach, Amir Globerson
Graph Neural Networks Use Graphs When They Shouldn't
Hofit Wasserman-Rozen, Ran Gilad-Bachrach, Niva Elkin-Koren
Lost in Translation: The Limits of Explainability in AI
Hofit Wasserman Rozen, Niva Elkin-Koren, and Ran Gilad-Bachrach
The Case Against Explainability
Yonatan E. Brand, Dafna Schwartz, Eran Gazit, Aron S. Buchman, Ran Gilad-Bachrach and Jeffrey M. Hausdorff
Gait Detection from a Wrist-Worn Sensor Using Machine Learning Methods: A Daily Living Study in Older Adults and People with Parkinson’s Disease
Maya Bechler-Speicher, Amir Globerson, Ran Gilad-Bachrach
Graph Trees with Attention
Nimrod Harel, Ran Gilad-Bachrach, Uri Obolski
Inherent Inconsistencies of Feature Importance
Pierre Laforgue, Giulia Clerici, Nicol`o Cesa-Bianchi, Ran Gilad-Bachrach
A Last Switch Dependent Analysis of Satiation and Seasonality in Bandits
Christopher Pyles, Francois van Schalkwyk, Gerard J. Gorman, Marijan Beg, Lee Stott, Nir Levy and Ran Gilad-Bachrach
PyBryt: auto-assessment and auto-grading for computational thinking
Roy Hirsch, Ran Gilad-Bachrach
Trees with Attention for Set Prediction Tasks
Amnon Catav, Boyang Fu, Yazeed Zoabi, Ahuva Libi Weiss Meilik, Noam Shomron, Jason Ernst, Sriram Sankararaman, Ran Gilad-Bachrach
Marginal Contribution Feature Importance-an Axiomatic Approach for Explaining Data
Brit Youngmann, Elad Yom-Tov, Ran Gilad-Bachrach, Danny Karmon
Algorithmic copywriting: automated generation of health-related advertisements to improve their performance
2021
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2022
דלית קן־דרור פלדמן, קרין נהון, ורדית אבידן ורן גלעד בכרך
זיוף, זיוף עמוק (Deep-Fake), חוק הסרטונים ועקרון הפרשנות המצמצמת: מה הדין?
Maya Bechler-Speicher, Ido Amos, Ran Gilad-Bachrach, Amir Globerson
Graph Neural Networks Use Graphs When They Shouldn't
Hofit Wasserman-Rozen, Ran Gilad-Bachrach, Niva Elkin-Koren
Lost in Translation: The Limits of Explainability in AI
Hofit Wasserman Rozen, Niva Elkin-Koren, and Ran Gilad-Bachrach
The Case Against Explainability
Yonatan E. Brand, Dafna Schwartz, Eran Gazit, Aron S. Buchman, Ran Gilad-Bachrach and Jeffrey M. Hausdorff
Gait Detection from a Wrist-Worn Sensor Using Machine Learning Methods: A Daily Living Study in Older Adults and People with Parkinson’s Disease
Maya Bechler-Speicher, Amir Globerson, Ran Gilad-Bachrach
Graph Trees with Attention
Nimrod Harel, Ran Gilad-Bachrach, Uri Obolski
Inherent Inconsistencies of Feature Importance
Pierre Laforgue, Giulia Clerici, Nicol`o Cesa-Bianchi, Ran Gilad-Bachrach
A Last Switch Dependent Analysis of Satiation and Seasonality in Bandits
Christopher Pyles, Francois van Schalkwyk, Gerard J. Gorman, Marijan Beg, Lee Stott, Nir Levy and Ran Gilad-Bachrach
PyBryt: auto-assessment and auto-grading for computational thinking
Roy Hirsch, Ran Gilad-Bachrach
Trees with Attention for Set Prediction Tasks
Amnon Catav, Boyang Fu, Yazeed Zoabi, Ahuva Libi Weiss Meilik, Noam Shomron, Jason Ernst, Sriram Sankararaman, Ran Gilad-Bachrach
Marginal Contribution Feature Importance-an Axiomatic Approach for Explaining Data
Brit Youngmann, Elad Yom-Tov, Ran Gilad-Bachrach, Danny Karmon
Algorithmic copywriting: automated generation of health-related advertisements to improve their performance
2020
2019
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2018
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2017
דלית קן־דרור פלדמן, קרין נהון, ורדית אבידן ורן גלעד בכרך
זיוף, זיוף עמוק (Deep-Fake), חוק הסרטונים ועקרון הפרשנות המצמצמת: מה הדין?
המשפט
Maya Bechler-Speicher, Ido Amos, Ran Gilad-Bachrach, Amir Globerson
Graph Neural Networks Use Graphs When They Shouldn't
arXiv:2309.04332
Hofit Wasserman-Rozen, Ran Gilad-Bachrach, Niva Elkin-Koren
Lost in Translation: The Limits of Explainability in AI
SSRN 4531323
Hofit Wasserman Rozen, Niva Elkin-Koren, and Ran Gilad-Bachrach
The Case Against Explainability
arXiv:2305.12167
Yonatan E. Brand, Dafna Schwartz, Eran Gazit, Aron S. Buchman, Ran Gilad-Bachrach and Jeffrey M. Hausdorff
Gait Detection from a Wrist-Worn Sensor Using Machine Learning Methods: A Daily Living Study in Older Adults and People with Parkinson’s Disease
Sensors
Maya Bechler-Speicher, Amir Globerson, Ran Gilad-Bachrach
Graph Trees with Attention
arXiv:2207.02760
Nimrod Harel, Ran Gilad-Bachrach, Uri Obolski
Inherent Inconsistencies of Feature Importance
arXiv:2206.08204
Pierre Laforgue, Giulia Clerici, Nicol`o Cesa-Bianchi, Ran Gilad-Bachrach
A Last Switch Dependent Analysis of Satiation and Seasonality in Bandits
The 25th International Conference on
Artificial Intelligence and Statistics (AIStats)
Christopher Pyles, Francois van Schalkwyk, Gerard J. Gorman, Marijan Beg, Lee Stott, Nir Levy and Ran Gilad-Bachrach
PyBryt: auto-assessment and auto-grading for computational thinking
arXiv:2112.02144, 2021
Roy Hirsch, Ran Gilad-Bachrach
Trees with Attention for Set Prediction Tasks
International Conference on Machine Learning, 4250-4261
Amnon Catav, Boyang Fu, Yazeed Zoabi, Ahuva Libi Weiss Meilik, Noam Shomron, Jason Ernst, Sriram Sankararaman, Ran Gilad-Bachrach
Marginal Contribution Feature Importance-an Axiomatic Approach for Explaining Data
International Conference on Machine Learning, 1324-1335
Brit Youngmann, Elad Yom-Tov, Ran Gilad-Bachrach, Danny Karmon
Algorithmic copywriting: automated generation of health-related advertisements to improve their performance
Information Retrieval Journal 24 (3), 205-239
2016
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2015
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2014
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2013
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2012
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2011
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