| Xgboost: A scalable tree boosting system T Chen, C Guestrin Proceedings of the 22nd acm sigkdd international conference on knowledge …, 2016 | 14311 | 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 | 7226 | 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 | 2544 | 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 | 2209 | 2012 |
| Powergraph: Distributed graph-parallel computation on natural graphs JE Gonzalez, Y Low, H Gu, D Bickson, C Guestrin 10th {USENIX} Symposium on Operating Systems Design and Implementation …, 2012 | 2015 | 2012 |
| Max-margin Markov networks B Roller, C Taskar, D Guestrin Advances in neural information processing systems 16, 25, 2004 | 1782 | 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 (2), 2008 | 1561 | 2008 |
| 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 | 1537 | 2004 |
| Graphchi: Large-scale graph computation on just a {PC} A Kyrola, G Blelloch, C Guestrin 10th {USENIX} Symposium on Operating Systems Design and Implementation …, 2012 | 1230 | 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 | 964 | 2014 |
| Anchors: High-precision model-agnostic explanations MT Ribeiro, S Singh, C Guestrin Proceedings of the AAAI conference on artificial intelligence 32 (1), 2018 | 922 | 2018 |
| 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 | 645 | 2005 |
| {TVM}: An automated end-to-end optimizing compiler for deep learning T Chen, T Moreau, Z Jiang, L Zheng, E Yan, H Shen, M Cowan, L Wang, ... 13th {USENIX} Symposium on Operating Systems Design and Implementation …, 2018 | 620 | 2018 |
| Stochastic gradient hamiltonian monte carlo T Chen, E Fox, C Guestrin International conference on machine learning, 1683-1691, 2014 | 604 | 2014 |
| Efficient solution algorithms for factored MDPs C Guestrin, D Koller, R Parr, S Venkataraman Journal of Artificial Intelligence Research 19, 399-468, 2003 | 587 | 2003 |
| 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 | 578 | 2004 |
| 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 | 569 | 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 | 551 | 2006 |
| Learning a predictable and generative vector representation for objects R Girdhar, DF Fouhey, M Rodriguez, A Gupta European Conference on Computer Vision, 484-499, 2016 | 542 | 2016 |
| Multiagent Planning with Factored MDPs. C Guestrin, D Koller, R Parr NIPS 1, 1523-1530, 2001 | 539 | 2001 |
Andreas KrauseProfessor of Computer Science, ETH ZurichVerified email at inf.ethz.ch
