| Collective classification in network data P Sen, G Namata, M Bilgic, L Getoor, B Galligher, T Eliassi-Rad AI magazine 29 (3), 93-93, 2008 | 4595 | 2008 |
| A survey of the state of explainable AI for natural language processing M Danilevsky, K Qian, R Aharonov, Y Katsis, B Kawas, P Sen arXiv preprint arXiv:2010.00711, 2020 | 483 | 2020 |
| Representing and querying correlated tuples in probabilistic databases P Sen, A Deshpande 2007 IEEE 23rd international conference on data engineering, 596-605, 2006 | 316 | 2006 |
| Systemml: Declarative machine learning on spark M Boehm, MW Dusenberry, D Eriksson, AV Evfimievski, FM Manshadi, ... Proceedings of the VLDB Endowment 9 (13), 1425-1436, 2016 | 259 | 2016 |
| PrDB: managing and exploiting rich correlations in probabilistic databases P Sen, A Deshpande, L Getoor The VLDB Journal 18, 1065-1090, 2009 | 150 | 2009 |
| Entity disambiguation with hierarchical topic models SS Kataria, KS Kumar, RR Rastogi, P Sen, SH Sengamedu Proceedings of the 17th ACM SIGKDD international conference on Knowledge …, 2011 | 124 | 2011 |
| Hybrid parallelization strategies for large-scale machine learning in systemml M Boehm, S Tatikonda, B Reinwald, P Sen, Y Tian, DR Burdick, ... Proceedings of the VLDB Endowment 7 (7), 553-564, 2014 | 111 | 2014 |
| Link-based classification P Sen, L Getoor UMIACS, 2007 | 108 | 2007 |
| Active learning for large-scale entity resolution K Qian, L Popa, P Sen Proceedings of the 2017 ACM on Conference on Information and Knowledge …, 2017 | 98 | 2017 |
| Exploiting shared correlations in probabilistic databases P Sen, A Deshpande, L Getoor Proceedings of the VLDB Endowment 1 (1), 809-820, 2008 | 97 | 2008 |
| A comprehensive benchmark framework for active learning methods in entity matching VV Meduri, L Popa, P Sen, M Sarwat Proceedings of the 2020 ACM SIGMOD international conference on management of …, 2020 | 78 | 2020 |
| Bisimulation-based approximate lifted inference P Sen, A Deshpande, L Getoor arXiv preprint arXiv:1205.2616, 2012 | 73 | 2012 |
| On optimizing operator fusion plans for large-scale machine learning in systemml M Boehm, B Reinwald, D Hutchison, AV Evfimievski, P Sen arXiv preprint arXiv:1801.00829, 2018 | 72 | 2018 |
| Collective context-aware topic models for entity disambiguation P Sen Proceedings of the 21st international conference on World Wide Web, 729-738, 2012 | 66 | 2012 |
| Read-once functions and query evaluation in probabilistic databases P Sen, A Deshpande, L Getoor Proceedings of the VLDB Endowment 3 (1-2), 1068-1079, 2010 | 64 | 2010 |
| Neuro-symbolic inductive logic programming with logical neural networks P Sen, BWSR de Carvalho, R Riegel, A Gray Proceedings of the AAAI conference on artificial intelligence 36 (8), 8212-8219, 2022 | 63 | 2022 |
| Community detection in content-sharing social networks N Natarajan, P Sen, V Chaoji Proceedings of the 2013 IEEE/ACM international conference on advances in …, 2013 | 59 | 2013 |
| SPOOF: Sum-Product Optimization and Operator Fusion for Large-Scale Machine Learning. T Elgamal, S Luo, M Boehm, AV Evfimievski, S Tatikonda, B Reinwald, ... CIDR 2 (6), 25, 2017 | 56 | 2017 |
| A Study on Interaction in Human-in-the-Loop Machine Learning for Text Analytics. Y Yang, E Kandogan, Y Li, P Sen, WS Lasecki IUI Workshops, 2019 | 49 | 2019 |
| SystemML's Optimizer: Plan Generation for Large-Scale Machine Learning Programs. M Boehm, DR Burdick, AV Evfimievski, B Reinwald, FR Reiss, P Sen, ... IEEE Data Eng. Bull. 37 (3), 52-62, 2014 | 49 | 2014 |
Lise GetoorDistinguished Professor of Computer Science & Engineering, University of California, Santa CruzEmail verificata su ucsc.edu
