| Deep learning for entity matching: A design space exploration S Mudgal, H Li, T Rekatsinas, AH Doan, Y Park, G Krishnan, R Deep, ... Proceedings of the 2018 international conference on management of data, 19-34, 2018 | 708 | 2018 |
| Holoclean: Holistic data repairs with probabilistic inference T Rekatsinas, X Chu, IF Ilyas, C Ré arXiv preprint arXiv:1702.00820, 2017 | 560 | 2017 |
| Data integration and machine learning: A natural synergy XL Dong, T Rekatsinas Proceedings of the 2018 international conference on management of data, 1645 …, 2018 | 193 | 2018 |
| Holodetect: Few-shot learning for error detection A Heidari, J McGrath, IF Ilyas, T Rekatsinas Proceedings of the 2019 International Conference on Management of Data, 829-846, 2019 | 163 | 2019 |
| P3: Distributed deep graph learning at scale S Gandhi, AP Iyer 15th {USENIX} Symposium on Operating Systems Design and Implementation …, 2021 | 160 | 2021 |
| HawkesTopic: A Joint Model for Network Inference and Topic Modeling from Text-Based Cascades X He, T Rekatsinas, J Foulds, L Getoor, Y Liu | 125 | 2015 |
| Fonduer: Knowledge base construction from richly formatted data S Wu, L Hsiao, X Cheng, B Hancock, T Rekatsinas, P Levis, C Ré Proceedings of the 2018 international conference on management of data, 1301 …, 2018 | 120 | 2018 |
| Characterizing and selecting fresh data sources T Rekatsinas, XL Dong, D Srivastava Proceedings of the 2014 ACM SIGMOD international conference on Management of …, 2014 | 98 | 2014 |
| Attention-based learning for missing data imputation in HoloClean R Wu, A Zhang, I Ilyas, T Rekatsinas Proceedings of Machine Learning and Systems 2, 307-325, 2020 | 81 | 2020 |
| Slimfast: Guaranteed results for data fusion and source reliability T Rekatsinas, M Joglekar, H Garcia-Molina, A Parameswaran, C Ré Proceedings of the 2017 ACM International Conference on Management of Data …, 2017 | 79 | 2017 |
| Finding quality in quantity: the challenge of discovering valuable sources for integration. T Rekatsinas, XL Dong, L Getoor, D Srivastava CIDR, 2015 | 73 | 2015 |
| Marius: Learning massive graph embeddings on a single machine J Mohoney, R Waleffe, H Xu, T Rekatsinas, S Venkataraman 15th {USENIX} Symposium on Operating Systems Design and Implementation …, 2021 | 67 | 2021 |
| A formal framework for probabilistic unclean databases C De Sa, IF Ilyas, B Kimelfeld, C Ré, T Rekatsinas arXiv preprint arXiv:1801.06750, 2018 | 56 | 2018 |
| Machine learning and data cleaning: Which serves the other? IF Ilyas, T Rekatsinas ACM Journal of Data and Information Quality (JDIQ) 14 (3), 1-11, 2022 | 50 | 2022 |
| Marius++: Large-scale training of graph neural networks on a single machine R Waleffe, J Mohoney, T Rekatsinas, S Venkataraman arXiv preprint arXiv:2202.02365 8, 2022 | 44* | 2022 |
| A statistical perspective on discovering functional dependencies in noisy data Y Zhang, Z Guo, T Rekatsinas Proceedings of the 2020 ACM SIGMOD International Conference on Management of …, 2020 | 39* | 2020 |
| SourceSeer: Forecasting Rare Disease Outbreaks Using Multiple Data Sources T Rekatsinas, S Ghosh, SR Mekaru, EO Nsoesie, JS Brownstein, L Getoor, ... Timeline 7, 8, 2015 | 39 | 2015 |
| Saga: A platform for continuous construction and serving of knowledge at scale IF Ilyas, T Rekatsinas, V Konda, J Pound, X Qi, M Soliman Proceedings of the 2022 international conference on management of data, 2259 …, 2022 | 37 | 2022 |
| Sysml: The new frontier of machine learning systems A Ratner, D Alistarh, G Alonso, P Bailis, S Bird, N Carlini, B Catanzaro, ... arXiv preprint arXiv:1904.03257 98, 2019 | 29 | 2019 |
| Picket: guarding against corrupted data in tabular data during learning and inference Z Liu, Z Zhou, T Rekatsinas The VLDB Journal 31 (5), 927-955, 2022 | 28* | 2022 |
Ihab F. IlyasUniversity of WaterlooVerified email at uwaterloo.ca
