| 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 | 431 | 2018 |
| Holoclean: Holistic data repairs with probabilistic inference T Rekatsinas, X Chu, IF Ilyas, C Ré arXiv preprint arXiv:1702.00820, 2017 | 366 | 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 | 117 | 2018 |
| HawkesTopic: A Joint Model for Network Inference and Topic Modeling from Text-Based Cascades X He, T Rekatsinas, J Foulds, L Getoor, Y Liu | 109 | 2015 |
| 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 | 93 | 2019 |
| 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 | 92 | 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 | 83 | 2014 |
| 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 | 65 | 2017 |
| Finding quality in quantity: the challenge of discovering valuable sources for integration. T Rekatsinas, XL Dong, L Getoor, D Srivastava CIDR, 2015 | 59 | 2015 |
| A formal framework for probabilistic unclean databases C De Sa, IF Ilyas, B Kimelfeld, C Ré, T Rekatsinas arXiv preprint arXiv:1801.06750, 2018 | 40 | 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 | 36 | 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 | 35 | 2015 |
| Multi-relational learning using weighted tensor decomposition with modular loss B London, T Rekatsinas, B Huang, L Getoor arXiv preprint arXiv:1303.1733, 2013 | 24 | 2013 |
| Sysml: The new frontier of machine learning systems A Ratner, D Alistarh, G Alonso, DG Andersen, P Bailis, S Bird, N Carlini, ... | 23 | 2019 |
| 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 | 21 | 2021 |
| SPARSI: Partitioning sensitive data amongst multiple adversaries T Rekatsinas, A Deshpande, A Machanavajjhala Proceedings of the VLDB Endowment 6 (13), 1594-1605, 2013 | 21 | 2013 |
| Sourcesight: Enabling effective source selection T Rekatsinas, A Deshpande, XL Dong, L Getoor, D Srivastava Proceedings of the 2016 International Conference on Management of Data, 2157 …, 2016 | 17 | 2016 |
| 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 | 14* | 2020 |
| Data-dependent differentially private parameter learning for directed graphical models AR Chowdhury, T Rekatsinas, S Jha International Conference on Machine Learning, 1939-1951, 2020 | 12 | 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 | 11 | 2019 |
Ihab F. IlyasUniversity of WaterlooVerified email at uwaterloo.ca
