| Explainable k-means and k-medians clustering M Moshkovitz, S Dasgupta, C Rashtchian, N Frost International conference on machine learning, 7055-7065, 2020 | 149 | 2020 |
| ExKMC: Expanding Explainable -Means Clustering N Frost, M Moshkovitz, C Rashtchian arXiv preprint arXiv:2006.02399, 2020 | 59 | 2020 |
| Provenance for natural language queries D Deutch, N Frost, A Gilad Proceedings of the VLDB Endowment 10 (5), 577-588, 2017 | 41 | 2017 |
| Framework for Evaluating Faithfulness of Local Explanations S Dasgupta, N Frost, M Moshkovitz Proceedings of the 39th International Conference on Machine Learning 162 …, 2022 | 32 | 2022 |
| Constraints-based explanations of classifications D Deutch, N Frost 2019 IEEE 35th International Conference on Data Engineering (ICDE), 530-541, 2019 | 21 | 2019 |
| Explaining natural language query results D Deutch, N Frost, A Gilad The VLDB Journal 29 (1), 485-508, 2020 | 20 | 2020 |
| Computing the Shapley value of facts in query answering D Deutch, N Frost, B Kimelfeld, M Monet Proceedings of the 2022 International Conference on Management of Data, 1570 …, 2022 | 15 | 2022 |
| Explainable k-means clustering: Theory and practice S Dasgupta, N Frost, M Moshkovitz, C Rashtchian XXAI Workshop. ICML, 2020 | 14 | 2020 |
| Nlprov: Natural language provenance D Deutch, N Frost, A Gilad Proceedings of the VLDB Endowment 9 (13), 1537-1540, 2016 | 13 | 2016 |
| Explanations for data repair through shapley values D Deutch, N Frost, A Gilad, O Sheffer Proceedings of the 30th ACM International Conference on Information …, 2021 | 11 | 2021 |
| Nlprovenans: natural language provenance for non-answers D Deutch, N Frost, A Gilad, T Haimovich Proceedings of the VLDB Endowment 11 (12), 1986-1989, 2018 | 10 | 2018 |
| Explaining Missing Query Results in Natural Language. D Deutch, N Frost, A Gilad, T Haimovich, A Bonifati, Y Zhou, MAV Salles, ... EDBT, 427-430, 2020 | 7 | 2020 |
| Personal insights for altering decisions of tree-based ensembles over time N Boer, D Deutch, N Frost, T Milo Proceedings of the VLDB Endowment 13 (6), 798-811, 2020 | 7 | 2020 |
| CEC: Constraints based explanation for classifications D Deutch, N Frost Proceedings of the 27th ACM International Conference on Information and …, 2018 | 7 | 2018 |
| LearnShapley: Learning to predict rankings of facts contribution based on query logs D Arad, D Deutch, N Frost Proceedings of the 31st ACM International Conference on Information …, 2022 | 5 | 2022 |
| ShapGraph: An holistic view of explanations through provenance graphs and Shapley values S Davidson, D Deutch, N Frost, B Kimelfeld, O Koren, M Monet Proceedings of the 2022 International Conference on Management of Data, 2373 …, 2022 | 5 | 2022 |
| SmartTriage: A system for personalized patient data capture, documentation generation, and decision support I Valmianski, N Frost, N Sood, Y Wang, B Liu, JJ Zhu, S Karumuri, IM Finn, ... Machine Learning for Health, 75-96, 2021 | 5 | 2021 |
| T-rex: Table repair explanations D Deutch, N Frost, A Gilad, O Sheffer Proceedings of the 2020 ACM SIGMOD International Conference on Management of …, 2020 | 4 | 2020 |
| Just in time: Personal temporal insights for altering model decisions N Boer, D Deutch, N Frost, T Milo 2019 IEEE 35th International Conference on Data Engineering (ICDE), 1988-1991, 2019 | 4 | 2019 |
| Provenance for Non-Experts. D Deutch, N Frost, A Gilad IEEE Data Eng. Bull. 41 (1), 3-14, 2018 | 3 | 2018 |
Daniel DeutchTel Aviv UniversityVerified email at post.tau.ac.il
