Carlos Guestrin
Carlos Guestrin
Amazon Professor of Machine Learning, University of Washington
Verified email at cs.washington.edu - Homepage
Title
Cited by
Cited by
Year
Xgboost: A scalable tree boosting system
T Chen, C Guestrin
Proceedings of the 22nd acm sigkdd international conference on knowledge …, 2016
62642016
" 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
33042016
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
21192007
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
18832012
Max-margin Markov networks
B Taskar, C Guestrin, D Koller
Advances in neural information processing systems, 25-32, 2004
16272004
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
14292004
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
12962012
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 (Feb), 235-284, 2008
12932008
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
10122012
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
8322014
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
5862005
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
5442004
Efficient solution algorithms for factored MDPs
C Guestrin, D Koller, R Parr, S Venkataraman
Journal of Artificial Intelligence Research 19, 399-468, 2003
5232003
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
5032008
Near-optimal sensor placements: Maximizing information while minimizing communication cost
A Krause, C Guestrin, A Gupta, J Kleinberg
Proceedings of the 5th international conference on Information processing in …, 2006
5022006
Multiagent planning with factored MDPs
C Guestrin, D Koller, R Parr
Advances in neural information processing systems, 1523-1530, 2002
4812002
Near-optimal sensor placements in gaussian processes
C Guestrin, A Krause, AP Singh
Proceedings of the 22nd international conference on Machine learning, 265-272, 2005
4712005
Near-optimal nonmyopic value of information in graphical models
A Krause, CE Guestrin
arXiv preprint arXiv:1207.1394, 2012
4252012
Graphlab: A new parallel framework for machine learning
Y Low, J Gonzalez, A Kyrola, D Bickson, C Guestrin, JM Hellerstein
Conference on uncertainty in artificial intelligence (UAI) 20, 2010
4062010
Stochastic gradient hamiltonian monte carlo
T Chen, E Fox, C Guestrin
International conference on machine learning, 1683-1691, 2014
3912014
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