| XGBoost: A Scalable Tree Boosting System T Chen Cornell University, 2016 | 70549 | 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 | 29528 | 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 | 3450 | 2007 |
| Stanford alpaca: An instruction-following llama model R Taori, I Gulrajani, T Zhang, Y Dubois, X Li, C Guestrin, P Liang, ... | 3419 | 2023 |
| Anchors: High-precision model-agnostic explanations MT Ribeiro, S Singh, C Guestrin Proceedings of the AAAI conference on artificial intelligence 32 (1), 2018 | 3301 | 2018 |
| {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 (OSDI …, 2012 | 2684 | 2012 |
| {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 (OSDI …, 2018 | 2651 | 2018 |
| 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 | 2582 | 2012 |
| Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining T Chen, C Guestrin ACM, 2016 | 2273 | 2016 |
| 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 | 2240 | 2008 |
| Max-margin Markov networks B Taskar, C Guestrin, D Koller Advances in neural information processing systems 16, 2003 | 1839 | 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 | 1629 | 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 (OSDI …, 2012 | 1605 | 2012 |
| Model-agnostic interpretability of machine learning MT Ribeiro, S Singh, C Guestrin arXiv preprint arXiv:1606.05386, 2016 | 1570 | 2016 |
| Beyond accuracy: Behavioral testing of NLP models with CheckList MT Ribeiro, T Wu, C Guestrin, S Singh arXiv preprint arXiv:2005.04118, 2020 | 1500 | 2020 |
| Training deep nets with sublinear memory cost T Chen, B Xu, C Zhang, C Guestrin arXiv preprint arXiv:1604.06174, 2016 | 1416 | 2016 |
| Stochastic gradient hamiltonian monte carlo T Chen, E Fox, C Guestrin International conference on machine learning, 1683-1691, 2014 | 1301 | 2014 |
| 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 | 1153 | 2014 |
| Alpacaeval: An automatic evaluator of instruction-following models X Li, T Zhang, Y Dubois, R Taori, I Gulrajani, C Guestrin, P Liang, ... | 895 | 2023 |
| Alpaca: A strong, replicable instruction-following model R Taori, I Gulrajani, T Zhang, Y Dubois, X Li, C Guestrin, P Liang, ... Stanford Center for Research on Foundation Models. https://crfm. stanford …, 2023 | 806 | 2023 |
Tianqi ChenCarnegie Mellon UniversityVerified email at cmu.edu
