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Theodoros Rekatsinas
Theodoros Rekatsinas
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Title
Cited by
Cited by
Year
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
6302018
Holoclean: Holistic data repairs with probabilistic inference
T Rekatsinas, X Chu, IF Ilyas, C Ré
arXiv preprint arXiv:1702.00820, 2017
5002017
Data integration and machine learning: A natural synergy
XL Dong, T Rekatsinas
Proceedings of the 2018 international conference on management of data, 1645 …, 2018
1702018
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
1482019
P3: Distributed deep graph learning at scale
S Gandhi, AP Iyer
15th {USENIX} Symposium on Operating Systems Design and Implementation …, 2021
1252021
HawkesTopic: A Joint Model for Network Inference and Topic Modeling from Text-Based Cascades
X He, T Rekatsinas, J Foulds, L Getoor, Y Liu
1242015
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
1162018
Characterizing and selecting fresh data sources
T Rekatsinas, XL Dong, D Srivastava
Proceedings of the 2014 ACM SIGMOD international conference on Management of …, 2014
952014
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
792017
Finding Quality in Quantity: The Challenge of Discovering Valuable Sources for Integration.
T Rekatsinas, XL Dong, L Getoor, D Srivastava
CIDR, 2015
682015
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
642020
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
582021
A formal framework for probabilistic unclean databases
C De Sa, IF Ilyas, B Kimelfeld, C Ré, T Rekatsinas
arXiv preprint arXiv:1801.06750, 2018
502018
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
372015
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 10, 2022
31*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
302020
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
292022
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
292019
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
24*2022
Multi-relational learning using weighted tensor decomposition with modular loss
B London, T Rekatsinas, B Huang, L Getoor
arXiv preprint arXiv:1303.1733, 2013
242013
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Articles 1–20