Daniel Lowd

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Pedro DomingosProfessor of Computer Science and Engineering, University of WashingtonVerified email at cs.washington.edu
Dejing DouBCG X, Baidu Research, University of Oregon, YaleVerified email at cs.uoregon.edu
Hoifung PoonGeneral Manager, Microsoft Health FuturesVerified email at microsoft.com
Javid EbrahimiAppleVerified email at apple.com
Matthew RichardsonMicrosoft ResearchVerified email at microsoft.com
Parag SinglaIndian Institute of Technology DelhiVerified email at cse.iitd.ac.in
Stanley KokNational University of SingaporeVerified email at nus.edu.sg
Pedram RooshenasUniversity of Illinois ChicagoVerified email at uic.edu
Zayd HammoudehUniversity of OregonVerified email at uoregon.edu
Jonathan BrophyUniversity of OregonVerified email at cs.uoregon.edu
Christopher MeekPrinciple Researcher, Microsoft ResearchVerified email at microsoft.com
Anyi RaoPostdoc, Stanford UniversityVerified email at stanford.edu
Aniruddh NathGoogle, LLCVerified email at google.com
Jesse DavisProfessor, Department of Computer Science, KU LeuvenVerified email at cs.kuleuven.be
Ali TorkamaniUniversity of Oregon, Cambia Health Solutions, Amazon Web ServicesVerified email at amazon.com
Sriraam NatarajanUniversity of Texas at DallasVerified email at utdallas.edu
Reza RejaieProfessor of Computer and Information Science, University of OregonVerified email at cs.uoregon.edu
Kristian KerstingProfessor of AI & ML, TU Darmstadt, Co-Director hessian.ai, DFKI, Germany, CLAIRE & ELLISVerified email at cs.tu-darmstadt.de
Prasad TadepalliProfessor of Computer Science, Oregon State UniversityVerified email at eecs.oregonstate.edu
Tushar KhotAllen Institute for AI; University of Wisconsin-MadisonVerified email at cs.wisc.edu

Daniel Lowd

Associate Professor of Computer Science, University of Oregon

Verified email at cs.uoregon.edu - Homepage

Machine learning artificial intelligence


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
Adversarial learning D Lowd, C Meek Proceedings of the eleventh ACM SIGKDD international conference on Knowledge …, 2005	970	2005
Hotflip: White-box adversarial examples for text classification J Ebrahimi, A Rao, D Lowd, D Dou arXiv preprint arXiv:1712.06751, 2017	947	2017
Markov logic: An interface layer for artificial intelligence P Domingos, D Lowd Synthesis Lectures on Artificial Intelligence and Machine Learning 3 (1), 1-155, 2009	617*	2009
Naive Bayes models for probability estimation D Lowd, P Domingos Proceedings of the 22nd international conference on Machine learning, 529-536, 2005	423	2005
Good word attacks on statistical spam filters D Lowd, C Meek Proceedings of the second conference on email and anti-spam (CEAS), 125-132, 2005	404	2005
The Alchemy system for statistical relational AI S Kok, P Singla, M Richardson, P Domingos, M Sumner, H Poon, D Lowd University of Washington, Seattle, 2005	367	2005
Efficient weight learning for Markov logic networks D Lowd, P Domingos Knowledge Discovery in Databases: PKDD 2007, 200-211, 2007	315	2007
Neural-symbolic learning and reasoning: A survey and interpretation AA Garcez, S Bader, H Bowman, LC Lamb, L de Penning, BV Illuminoo, ... Neuro-Symbolic Artificial Intelligence: The State of the Art 342 (1), 327, 2022	296	2022
On adversarial examples for character-level neural machine translation J Ebrahimi, D Lowd, D Dou arXiv preprint arXiv:1806.09030, 2018	229	2018
Learning sum-product networks with direct and indirect variable interactions A Rooshenas, D Lowd International Conference on Machine Learning, 710-718, 2014	188	2014
Unifying logical and statistical AI P Domingos, D Lowd, S Kok, A Nath, H Poon, M Richardson, P Singla Proceedings of the 31st Annual ACM/IEEE Symposium on Logic in Computer …, 2016	162	2016
Learning arithmetic circuits D Lowd, P Domingos Proc. UAI 8, 383-392, 2008	120*	2008
Markov logic P Domingos, S Kok, D Lowd, H Poon, M Richardson, P Singla Probabilistic inductive logic programming, 92-117, 2008	104	2008
Learning Markov network structure with decision trees D Lowd, J Davis Data Mining (ICDM), 2010 IEEE 10th International Conference on, 334-343, 2010	98	2010
Machine unlearning for random forests J Brophy, D Lowd International Conference on Machine Learning, 1092-1104, 2021	90	2021
Learning to refine an automatically extracted knowledge base using markov logic S Jiang, D Lowd, D Dou 2012 IEEE 12th International Conference on Data Mining, 912-917, 2012	84	2012
Learning Markov Networks With Arithmetic Circuits D Lowd, A Rooshenas 16th International Conference on Artificial Intelligence and Statistics …, 2013	76	2013
A joint sentiment-target-stance model for stance classification in tweets J Ebrahimi, D Dou, D Lowd Proceedings of COLING 2016, the 26th international conference on …, 2016	70	2016
Weakly supervised tweet stance classification by relational bootstrapping J Ebrahimi, D Dou, D Lowd Proceedings of the 2016 conference on empirical methods in natural language …, 2016	65	2016
Just add weights: Markov logic for the semantic web P Domingos, D Lowd, S Kok, H Poon, M Richardson, P Singla Uncertainty Reasoning for the Semantic Web I: ISWC International Workshops …, 2008	54	2008

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