| Holoclean: Holistic data repairs with probabilistic inference T Rekatsinas, X Chu, IF Ilyas, C Ré arXiv preprint arXiv:1702.00820, 2017 | 206 | 2017 |
| 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 | 197 | 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 | 87 | 2015 |
| Characterizing and selecting fresh data sources T Rekatsinas, XL Dong, D Srivastava Proceedings of the 2014 ACM SIGMOD international conference on Management of …, 2014 | 82 | 2014 |
| 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 | 59 | 2018 |
| Finding Quality in Quantity: The Challenge of Discovering Valuable Sources for Integration. T Rekatsinas, XL Dong, L Getoor, D Srivastava CIDR, 2015 | 54 | 2015 |
| Data integration and machine learning: A natural synergy XL Dong, T Rekatsinas Proceedings of the 2018 international conference on management of data, 1645 …, 2018 | 47 | 2018 |
| 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 | 44 | 2017 |
| 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 | 42 | 2019 |
| 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 | 29 | 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 | 28 | 2018 |
| 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 |
| Sparsi: Partitioning sensitive data amongst multiple adversaries T Rekatsinas, A Deshpande, A Machanavajjhala Proceedings of the VLDB Endowment 6 (13), 1594-1605, 2013 | 18 | 2013 |
| SysML: The New Frontier of Machine Learning Systems. A Ratner, D Alistarh, G Alonso, DG Andersen, P Bailis, S Bird, N Carlini, ... | 16 | 2019 |
| 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 | 15 | 2016 |
| 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 | 11 | 2020 |
| StoryPivot: comparing and contrasting story evolution A Gruenheid, D Kossmann, T Rekatsinas, D Srivastava Proceedings of the 2015 ACM SIGMOD International Conference on Management of …, 2015 | 9 | 2015 |
| Local structure and determinism in probabilistic databases T Rekatsinas, A Deshpande, L Getoor Proceedings of the 2012 ACM SIGMOD International Conference on Management of …, 2012 | 9 | 2012 |
| Crowdgather: Entity extraction over structured domains T Rekatsinas, A Deshpande, A Parameswaran arXiv preprint arXiv:1502.06823, 2015 | 8 | 2015 |
| Fuzzy rule based neuro-dynamic programming for mobile robot skill acquisition on the basis of a nested multi-agent architecture JN Karigiannis, TI Rekatsinas, CS Tzafestas 2010 IEEE International Conference on Robotics and Biomimetics, 312-319, 2010 | 7 | 2010 |
