Carlos Guestrin
Carlos Guestrin
Amazon Professor of Machine Learning, University of Washington
Verified email at cs.washington.edu - Homepage
TitleCited byYear
Xgboost: A scalable tree boosting system
T Chen, C Guestrin
Proceedings of the 22nd acm sigkdd international conference on knowledge …, 2016
30612016
Cost-effective outbreak detection in networks
J Leskovec, A Krause, C Guestrin, C Faloutsos, C Faloutsos, ...
Proceedings of the 13th ACM SIGKDD international conference on Knowledge …, 2007
18802007
Distributed GraphLab: a framework for machine learning and data mining in the cloud
Y Low, D Bickson, J Gonzalez, C Guestrin, A Kyrola, JM Hellerstein
Proceedings of the VLDB Endowment 5 (8), 716-727, 2012
16532012
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
16462016
Max-margin Markov networks
B Taskar, C Guestrin, D Koller
Advances in neural information processing systems, 25-32, 2004
15562004
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
13982004
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
11312012
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
11032008
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
8822012
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
7532014
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
5302005
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
5192005
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
5092004
Efficient solution algorithms for factored MDPs
C Guestrin, D Koller, R Parr, S Venkataraman
Journal of Artificial Intelligence Research 19, 399-468, 2003
4822003
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
4792006
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
4542008
Multiagent planning with factored MDPs
C Guestrin, D Koller, R Parr
Advances in neural information processing systems, 1523-1530, 2002
4232002
Near-optimal nonmyopic value of information in graphical models
A Krause, CE Guestrin
arXiv preprint arXiv:1207.1394, 2012
3872012
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), 340-349, 2010
3852010
Coordinated reinforcement learning
C Guestrin, M Lagoudakis, R Parr
ICML 2, 227-234, 2002
3262002
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