Murat Kocaoglu

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Alexandros G DimakisProfessor, ECE University of Texas at AustinDirección de correo verificada de austin.utexas.edu
Karthikeyan ShanmugamResearch Scientist, Google Research IndiaDirección de correo verificada de google.com
Sriram VishwanathUniversity of Texas, MITREDirección de correo verificada de mitre.org
Ozgur B. AkanUniversity of Cambridge, UK, and Koç University, TurkeyDirección de correo verificada de cam.ac.uk
Kristjan GreenewaldResearch Scientist, MIT-IBM Watson AI Lab, IBM ResearchDirección de correo verificada de ibm.com
Elias BareinboimColumbia UniversityDirección de correo verificada de columbia.edu
Spencer ComptonStanford UniversityDirección de correo verificada de stanford.edu
Derya MalakEURECOM, Rensselaer Polytechnic InstituteDirección de correo verificada de eurecom.fr
Amin JaberPhD Student, Purdue UniversityDirección de correo verificada de purdue.edu
Christopher SnyderPathology resident at Washington University in St. LouisDirección de correo verificada de wustl.edu
Rajat SenGoogleDirección de correo verificada de utexas.edu
Babak HassibiMose and Lilian S. Bohn Professor of Electrical EngineeringDirección de correo verificada de caltech.edu
Sara MagliacaneUniversity of AmsterdamDirección de correo verificada de uva.nl
Mahsa GhasemiAssistant Professor of Electrical and Computer Engineering, Purdue UniversityDirección de correo verificada de purdue.edu
Qasim ElahiPhD. StudentDirección de correo verificada de purdue.edu
Constantine CaramanisProfessor of Electrical and Computer Engineering, UT AustinDirección de correo verificada de utexas.edu
Shiv SainiAdobe ResearchDirección de correo verificada de adobe.com
Saurabh BagchiElectrical and Computer Engineering, Computer Science; Director Army A2I2 & NSF CHORUS; PurdueDirección de correo verificada de purdue.edu
Subrata MitraAdobe ResearchDirección de correo verificada de adobe.com
Azam IkramGraduate Student at Purdue UniversityDirección de correo verificada de purdue.edu

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Murat Kocaoglu

Assistant Professor, School of ECE, Purdue University

Dirección de correo verificada de purdue.edu - Página principal

Causal Inference Machine Learning Information Theory


Título Ordenar por citas Ordenar por año Ordenar por título	Citado por Citado por	Año
Causalgan: Learning causal implicit generative models with adversarial training M Kocaoglu, C Snyder, AG Dimakis, S Vishwanath International Conference on Learning Representations (ICLR), 2018	315	2018
Learning causal graphs with small interventions K Shanmugam, M Kocaoglu, AG Dimakis, S Vishwanath Advances in Neural Information Processing Systems 28, 2015	114	2015
Causal discovery from soft interventions with unknown targets: Characterization and learning A Jaber, M Kocaoglu, K Shanmugam, E Bareinboim Advances in neural information processing systems 33, 9551-9561, 2020	111	2020
Experimental design for learning causal graphs with latent variables M Kocaoglu, K Shanmugam, E Bareinboim Advances in Neural Information Processing Systems 30, 2017	96	2017
Entropic causal inference M Kocaoglu, A Dimakis, S Vishwanath, B Hassibi Proceedings of the AAAI Conference on Artificial Intelligence 31 (1), 2017	79	2017
Cost-optimal learning of causal graphs M Kocaoglu, A Dimakis, S Vishwanath International Conference on Machine Learning, 1875-1884, 2017	75	2017
Root cause analysis of failures in microservices through causal discovery A Ikram, S Chakraborty, S Mitra, S Saini, S Bagchi, M Kocaoglu Advances in Neural Information Processing Systems 35, 31158-31170, 2022	67	2022
Characterization and learning of causal graphs with latent variables from soft interventions M Kocaoglu, A Jaber, K Shanmugam, E Bareinboim Advances in Neural Information Processing Systems 32, 2019	64	2019
Minimum energy channel codes for nanoscale wireless communications M Kocaoglu, OB Akan IEEE Transactions on Wireless Communications 12 (4), 1492-1500, 2013	57	2013
Contextual bandits with latent confounders: An nmf approach R Sen, K Shanmugam, M Kocaoglu, A Dimakis, S Shakkottai Artificial Intelligence and Statistics, 518-527, 2017	56	2017
Experimental design for cost-aware learning of causal graphs E Lindgren, M Kocaoglu, AG Dimakis, S Vishwanath Advances in Neural Information Processing Systems 31, 2018	51	2018
Sample efficient active learning of causal trees K Greenewald, D Katz, K Shanmugam, S Magliacane, M Kocaoglu, ... Advances in Neural Information Processing Systems 32, 2019	48	2019
Active structure learning of causal DAGs via directed clique trees C Squires, S Magliacane, K Greenewald, D Katz, M Kocaoglu, ... Advances in Neural Information Processing Systems 33, 21500-21511, 2020	39	2020
Communication theoretic analysis of the synaptic channel for cortical neurons D Malak, M Kocaoglu, OB Akan Nano Communication Networks 4 (3), 131-141, 2013	27	2013
Fundamentals of green communications and computing: Modeling and simulation M Kocaoglu, D Malak, OB Akan Computer 45 (9), 40-46, 2012	27	2012
Minimum energy coding for wireless nanosensor networks M Kocaoglu, OB Akan 2012 Proceedings IEEE INFOCOM, 2826-2830, 2012	26	2012
Applications of common entropy for causal inference M Kocaoglu, S Shakkottai, AG Dimakis, C Caramanis, S Vishwanath Advances in neural information processing systems 33, 17514-17525, 2020	23	2020
Entropic causality and greedy minimum entropy coupling M Kocaoglu, AG Dimakis, S Vishwanath, B Hassibi 2017 IEEE International Symposium on Information Theory (ISIT), 1465-1469, 2017	18	2017
Entropic causal inference: Identifiability and finite sample results S Compton, M Kocaoglu, K Greenewald, D Katz Advances in Neural Information Processing Systems 33, 14772-14782, 2020	16	2020
Sparse polynomial learning and graph sketching M Kocaoglu, K Shanmugam, AG Dimakis, A Klivans Advances in Neural Information Processing Systems 27, 2014	16	2014

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