| 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 |
| On Explaining Confounding Bias B Youngmann, M Cafarella, Y Moskovitch, B Salimi 2023 IEEE 39th International Conference on Data Engineering (ICDE), 1846-1859, 2023 | 26 | 2023 |
| A provenance framework for data-dependent process analysis D Deutch, Y Moskovitch, V Tannen Proceedings of the VLDB Endowment 7 (6), 457-468, 2014 | 23 | 2014 |
| On detecting cherry-picked generalizations Y Lin, B Youngmann, Y Moskovitch, HV Jagadish, T Milo Proceedings of the VLDB Endowment 15 (1), 59-71, 2021 | 22 | 2021 |
| 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 |
| Provenance-based analysis of data-centric processes D Deutch, Y Moskovitch, V Tannen The VLDB Journal 24 (4), 583-607, 2015 | 20 | 2015 |
| DENOUNCER: detection of unfairness in classifiers J Li, Y Moskovitch, HV Jagadish Proceedings of the VLDB Endowment 14 (12), 2021 | 19 | 2021 |
| Hypothetical reasoning via provenance abstraction D Deutch, Y Moskovitch, N Rinetzky Proceedings of the 2019 International Conference on Management of Data, 537-554, 2019 | 18 | 2019 |
| Efficient provenance tracking for datalog using top-k queries D Deutch, A Gilad, Y Moskovitch The VLDB Journal 27, 245-269, 2018 | 17 | 2018 |
| Query Refinement for Diversity Constraint Satisfaction J Li, Y Moskovitch, J Stoyanovich, HV Jagadish Proceedings of the VLDB Endowment 17 (2), 106-118, 2023 | 15 | 2023 |
| Countata: dataset labeling using pattern counts Y Moskovitch, HV Jagadish Proceedings of the VLDB Endowment 13 (12), 2020 | 14 | 2020 |
| Analyzing data-centric applications: Why, what-if, and how-to P Bourhis, D Deutch, Y Moskovitch 2016 IEEE 32nd International Conference on Data Engineering (ICDE), 779-790, 2016 | 14 | 2016 |
| selP: selective tracking and presentation of data provenance D Deutch, A Gilad, Y Moskovitch 2015 IEEE 31st International Conference on Data Engineering, 1484-1487, 2015 | 12 | 2015 |
| Detection of groups with biased representation in ranking J Li, Y Moskovitch, HV Jagadish 2023 IEEE 39th International Conference on Data Engineering (ICDE), 2167-2179, 2023 | 10 | 2023 |
| Equivalence-invariant algebraic provenance for hyperplane update queries P Bourhis, D Deutch, Y Moskovitch Proceedings of the 2020 ACM SIGMOD International Conference on Management of …, 2020 | 9 | 2020 |
| Reliability at multiple stages in a data analysis pipeline Y Moskovitch, HV Jagadish Communications of the ACM 65 (11), 118-128, 2022 | 7 | 2022 |
| PROPOLIS: provisioned analysis of data-centric processes D Deutch, Y Moskovitch, V Tannen Proceedings of the VLDB Endowment 6 (12), 1302-1305, 2013 | 7 | 2013 |
| Query Refinement for Diverse Top-k Selection FS Campbell, A Silberstein, J Stoyanovich, Y Moskovitch Proceedings of the ACM on Management of Data 2 (3), 1-27, 2024 | 6 | 2024 |
| NEXUS: On Explaining Confounding Bias B Youngmann, M Cafarella, Y Moskovitch, B Salimi Companion of the 2023 International Conference on Management of Data, 171-174, 2023 | 6 | 2023 |
| Patterns count-based labels for datasets Y Moskovitch, HV Jagadish 2021 IEEE 37th International Conference on Data Engineering (ICDE), 1961-1966, 2021 | 6 | 2021 |
