| Selective provenance for datalog programs using top-k queries D Deutch, A Gilad, Y Moskovitch Proceedings of the VLDB Endowment 8 (12), 1394-1405, 2015 | 53 | 2015 |
| Provenance for natural language queries D Deutch, N Frost, A Gilad Proceedings of the VLDB Endowment 10 (5), 577-588, 2017 | 45 | 2017 |
| QPlain: Query by explanation D Deutch, A Gilad 2016 IEEE 32nd International Conference on Data Engineering (ICDE), 1358-1361, 2016 | 33 | 2016 |
| Hyper: Hypothetical reasoning with what-if and how-to queries using a probabilistic causal approach S Galhotra, A Gilad, S Roy, B Salimi Proceedings of the 2022 International Conference on Management of Data, 1598 …, 2022 | 29 | 2022 |
| Explaining natural language query results D Deutch, N Frost, A Gilad The VLDB Journal 29 (1), 485-508, 2020 | 29 | 2020 |
| Reverse-Engineering Conjunctive Queries from Provenance Examples. D Deutch, A Gilad EDBT, 277-288, 2019 | 22 | 2019 |
| On Multiple Semantics for Declarative Database Repairs A Gilad, D Deutch, S Roy | 21* | |
| Prefair: Privately generating justifiably fair synthetic data D Pujol, A Gilad, A Machanavajjhala arXiv preprint arXiv:2212.10310, 2022 | 20 | 2022 |
| On optimizing the trade-off between privacy and utility in data provenance D Deutch, A Frankenthal, A Gilad, Y Moskovitch Proceedings of the 2021 International Conference on Management of Data, 379-391, 2021 | 20 | 2021 |
| Fedex: An explainability framework for data exploration steps D Deutch, A Gilad, T Milo, A Mualem, A Somech arXiv preprint arXiv:2209.06260, 2022 | 18 | 2022 |
| Synthesizing linked data under cardinality and integrity constraints A Gilad, S Patwa, A Machanavajjhala Proceedings of the 2021 International Conference on Management of Data, 619-631, 2021 | 18 | 2021 |
| 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 | 17 | 2021 |
| Efficient provenance tracking for datalog using top-k queries D Deutch, A Gilad, Y Moskovitch The VLDB Journal 27 (2), 245-269, 2018 | 17 | 2018 |
| ExplainED: explanations for EDA notebooks D Deutch, A Gilad, T Milo, A Somech Proceedings of the VLDB Endowment 13 (12), 2917-2920, 2020 | 15 | 2020 |
| Interactive inference of SPARQL queries using provenance E Abramovitz, D Deutch, A Gilad 2018 IEEE 34th International Conference on Data Engineering (ICDE), 581-592, 2018 | 14 | 2018 |
| Nlprov: Natural language provenance D Deutch, N Frost, A Gilad Proceedings of the VLDB Endowment 9 (13), 1537-1540, 2016 | 14 | 2016 |
| Dpxplain: Privately explaining aggregate query answers Y Tao, A Gilad, A Machanavajjhala, S Roy arXiv preprint arXiv:2209.01286, 2022 | 13 | 2022 |
| Summarized causal explanations for aggregate views B Youngmann, M Cafarella, A Gilad, S Roy Proceedings of the ACM on Management of Data 2 (1), 1-27, 2024 | 12 | 2024 |
| Understanding queries by conditional instances A Gilad, Z Miao, S Roy, J Yang Proceedings of the 2022 International Conference on Management of Data, 355-368, 2022 | 12 | 2022 |
| 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 | 12 | 2020 |
Daniel DeutchTel Aviv UniversityVerified email at post.tau.ac.il
