| 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 | 852 | 2018 |
| Holoclean: Holistic data repairs with probabilistic inference T Rekatsinas, X Chu, IF Ilyas, C Ré arXiv preprint arXiv:1702.00820, 2017 | 686 | 2017 |
| P3: Distributed deep graph learning at scale S Gandhi, AP Iyer 15th {USENIX} Symposium on Operating Systems Design and Implementation …, 2021 | 235 | 2021 |
| Data integration and machine learning: A natural synergy XL Dong, T Rekatsinas Proceedings of the 2018 international conference on management of data, 1645 …, 2018 | 224 | 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 | 213 | 2019 |
| HawkesTopic: A Joint Model for Network Inference and Topic Modeling from Text-Based Cascades X He, T Rekatsinas, J Foulds, L Getoor, Y Liu | 129 | 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 | 123 | 2018 |
| 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 | 101 | 2020 |
| 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 | 95 | 2021 |
| Characterizing and selecting fresh data sources T Rekatsinas, XL Dong, D Srivastava Proceedings of the 2014 ACM SIGMOD international conference on Management of …, 2014 | 94 | 2014 |
| 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 | 92 | 2022 |
| 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 | 86 | 2017 |
| 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 | 82* | 2022 |
| Finding Quality in Quantity: The Challenge of Discovering Valuable Sources for Integration. T Rekatsinas, XL Dong, L Getoor, D Srivastava CIDR, 2015 | 67 | 2015 |
| High-throughput vector similarity search in knowledge graphs J Mohoney, A Pacaci, SR Chowdhury, A Mousavi, IF Ilyas, UF Minhas, ... Proceedings of the ACM on Management of Data 1 (2), 1-25, 2023 | 61 | 2023 |
| A formal framework for probabilistic unclean databases C De Sa, IF Ilyas, B Kimelfeld, C Ré, T Rekatsinas arXiv preprint arXiv:1801.06750, 2018 | 60 | 2018 |
| 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 | 52 | 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 | 52 | 2020 |
| Mlsys: The new frontier of machine learning systems A Ratner, D Alistarh, G Alonso, DG Andersen, P Bailis, S Bird, N Carlini, ... arXiv preprint arXiv:1904.03257, 2019 | 52 | 2019 |
| Co-design hardware and algorithm for vector search W Jiang, S Li, Y Zhu, J de Fine Licht, Z He, R Shi, C Renggli, S Zhang, ... Proceedings of the International Conference for High Performance Computing …, 2023 | 46 | 2023 |
Ihab F. IlyasUniversity of WaterlooVerified email at uwaterloo.ca
