| Xgboost: A scalable tree boosting system T Chen, C Guestrin Proceedings of the 22nd acm sigkdd international conference on knowledge …, 2016 | 8814 | 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 | 4521 | 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 | 2279 | 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 | 1997 | 2012 |
| Max-margin Markov networks B Taskar, C Guestrin, D Koller Advances in neural information processing systems, 25-32, 2004 | 1708 | 2004 |
| 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 | 1459 | 2004 |
| 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 | 1411 | 2008 |
| 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 | 1391 | 2012 |
| 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 | 1085 | 2012 |
| 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 | 877 | 2014 |
| 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 | 612 | 2005 |
| 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 | 555 | 2004 |
| Efficient solution algorithms for factored MDPs C Guestrin, D Koller, R Parr, S Venkataraman Journal of Artificial Intelligence Research 19, 399-468, 2003 | 548 | 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 | 525 | 2008 |
| 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 | 522 | 2006 |
| Multiagent planning with factored MDPs C Guestrin, D Koller, R Parr Advances in neural information processing systems 14, 1523-1530, 2001 | 508 | 2001 |
| Anchors: High-precision model-agnostic explanations MT Ribeiro, S Singh, C Guestrin Proceedings of the AAAI Conference on Artificial Intelligence 32 (1), 2018 | 507 | 2018 |
| 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 | 493 | 2005 |
| Stochastic gradient hamiltonian monte carlo T Chen, E Fox, C Guestrin International conference on machine learning, 1683-1691, 2014 | 480 | 2014 |
| Near-optimal nonmyopic value of information in graphical models A Krause, CE Guestrin arXiv preprint arXiv:1207.1394, 2012 | 451 | 2012 |
Andreas KrauseProfessor of Computer Science, ETH ZurichVerified email at inf.ethz.ch
