Carlos Guestrin

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Andreas KrauseProfessor of Computer Science, ETH ZurichVerified email at inf.ethz.ch
Tianqi ChenCarnegie Mellon UniversityVerified email at cmu.edu
Sameer SinghAssociate Professor, University of California IrvineVerified email at uci.edu
Marco Tulio RibeiroMicrosoft ResearchVerified email at cs.washington.edu
Joseph HellersteinJim Gray Professor of Computer Science, UC BerkeleyVerified email at berkeley.edu
Danny BicksonSr. Manager, AppleVerified email at cs.cmu.edu
Aapo KyrolaPh.D (Carnegie Mellon University, 2014)Verified email at cs.cmu.edu
Daphne KollerCEO and Founder, insitroVerified email at insitro.com
Samuel MaddenMITVerified email at csail.mit.edu
Jure LeskovecProfessor of Computer Science, Stanford UniversityVerified email at cs.stanford.edu
Christos FaloutsosCMUVerified email at cs.cmu.edu
Jeanne M. VanBriesenDepartment of Civil and Environmental Engineering at Carnegie Mellon UniversityVerified email at andrew.cmu.edu
Ronald ParrProfessor of Computer Science, Duke UniversityVerified email at cs.duke.edu
Mark PaskinGoogleVerified email at google.com
Ben TaskarUniversity of WashingtonVerified email at cs.washington.edu
Dafna ShahafAssociate Professor of Computer Science, The Hebrew University of JerusalemVerified email at cs.huji.ac.il
Arvind KrishnamurthyShort-Dooley Professor, Univ. of WashingtonVerified email at cs.washington.edu
Luis CezeProfessor of Computer Science and Engineering, University of WashingtonVerified email at cs.washington.edu
Emily B. FoxStanford UniversityVerified email at stanford.edu
Jon KleinbergProfessor of Computer Science, Cornell UniversityVerified email at cs.cornell.edu

Carlos Guestrin

Professor, Stanford University

Verified email at stanford.edu - Homepage

Machine Learning Distributed Systems Artificial Intelligence Parallel Algorithms Sensor Networks


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Xgboost: A scalable tree boosting system T Chen, C Guestrin Proceedings of the 22nd acm sigkdd international conference on knowledge …, 2016	24274	2016
" Why should i trust you?" Explaining the predictions of any classifier MT Ribeiro, S Singh, C Guestrin Proceedings of the 22nd ACM SIGKDD international conference on knowledge …, 2016	11433	2016
Cost-effective outbreak detection in networks J Leskovec, A Krause, C Guestrin, C Faloutsos, J VanBriesen, N Glance Proceedings of the 13th ACM SIGKDD international conference on Knowledge …, 2007	2771	2007
Distributed graphlab: A framework for machine learning in the cloud Y Low, J Gonzalez, A Kyrola, D Bickson, C Guestrin, JM Hellerstein arXiv preprint arXiv:1204.6078, 2012	2365	2012
Powergraph: Distributed graph-parallel computation on natural graphs JE Gonzalez, Y Low, H Gu, D Bickson, C Guestrin Presented as part of the 10th {USENIX} Symposium on Operating Systems Design …, 2012	2229	2012
Near-optimal sensor placements in Gaussian processes: Theory, efficient algorithms and empirical studies. A Krause, A Singh, C Guestrin Journal of Machine Learning Research 9 (2), 2008	1742	2008
Max-margin Markov networks B Taskar, C Guestrin, D Koller Advances in neural information processing systems 16, 2003	1720	2003
Model-driven data acquisition in sensor networks A Deshpande, C Guestrin, SR Madden, JM Hellerstein, W Hong Proceedings of the Thirtieth international conference on Very large data …, 2004	1567	2004
Anchors: High-precision model-agnostic explanations MT Ribeiro, S Singh, C Guestrin Proceedings of the AAAI conference on artificial intelligence 32 (1), 2018	1490	2018
Graphchi: Large-scale graph computation on just a {PC} A Kyrola, G Blelloch, C Guestrin Presented as part of the 10th {USENIX} Symposium on Operating Systems Design …, 2012	1363	2012
TVM: An automated end-to-end optimizing compiler for deep learning T Chen, T Moreau, Z Jiang, L Zheng, E Yan, M Cowan, H Shen, L Wang, ... arXiv preprint arXiv:1802.04799, 2018	1066	2018
Graphlab: A new framework for parallel machine learning Y Low, JE Gonzalez, A Kyrola, D Bickson, CE Guestrin, J Hellerstein arXiv preprint arXiv:1408.2041, 2014	1030	2014
Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining T Chen, C Guestrin	867	2016
Stochastic gradient hamiltonian monte carlo T Chen, E Fox, C Guestrin International conference on machine learning, 1683-1691, 2014	772	2014
Model-agnostic interpretability of machine learning MT Ribeiro, S Singh, C Guestrin arXiv preprint arXiv:1606.05386, 2016	767	2016
Learning structured prediction models: A large margin approach B Taskar, V Chatalbashev, D Koller, C Guestrin Proceedings of the 22nd international conference on Machine learning, 896-903, 2005	654	2005
Efficient solution algorithms for factored MDPs C Guestrin, D Koller, R Parr, S Venkataraman Journal of Artificial Intelligence Research 19, 399-468, 2003	620	2003
The battle of the water sensor networks (BWSN): A design challenge for engineers and algorithms A Ostfeld, JG Uber, E Salomons, JW Berry, WE Hart, CA Phillips, ... Journal of water resources planning and management 134 (6), 556-568, 2008	619	2008
Distributed regression: an efficient framework for modeling sensor network data C Guestrin, P Bodik, R Thibaux, M Paskin, S Madden Proceedings of the 3rd international symposium on Information processing in …, 2004	594	2004
Multiagent planning with factored MDPs C Guestrin, D Koller, R Parr Advances in neural information processing systems 14, 2001	592	2001

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