| RAB: Provable Robustness Against Backdoor Attacks M Weber, X Xu, B Karlaš, C Zhang, B Li arXiv preprint arXiv:2003.08904, 2020 | 49 | 2020 |
| Is advance knowledge of flow sizes a plausible assumption? V Ðukić, SA Jyothi, B Karlaš, M Owaida, C Zhang, A Singla 16th USENIX Symposium on Networked Systems Design and Implementation (NSDI …, 2019 | 43 | 2019 |
| Continuous Integration of Machine Learning Models: A Rigorous Yet Practical Treatment C Renggli, B Karlaš, B Ding, F Liu, K Schawinski, W Wu, C Zhang Proceedings of Machine Learning and Systems, 2019 | 32 | 2019 |
| Data Science through the looking glass and what we found there F Psallidas, Y Zhu, B Karlaš, M Interlandi, A Floratou, K Karanasos, W Wu, ... arXiv preprint arXiv:1912.09536, 2019 | 20 | 2019 |
| Building continuous integration services for machine learning B Karlaš, M Interlandi, C Renggli, W Wu, C Zhang, ... Proceedings of the 26th ACM SIGKDD International Conference on Knowledge …, 2020 | 15 | 2020 |
| Nearest neighbor classifiers over incomplete information: From certain answers to certain predictions B Karlaš, P Li, R Wu, NM Gürel, X Chu, W Wu, C Zhang Proceedings of the VLDB Endowment 14 (3), 255-267, 2020 | 14 | 2020 |
| A data quality-driven view of mlops C Renggli, L Rimanic, NM Gürel, B Karlaš, W Wu, C Zhang IEEE Data Engineering Bulletin, 2021 | 13 | 2021 |
| Ease.ML: A Lifecycle Management System for MLDev and MLOps L Aguilar Melgar, D Dao, S Gan, NM Gürel, N Hollenstein, J Jiang, ... 11th Annual Conference on Innovative Data Systems Research (CIDR 2021), 2021 | 12* | 2021 |
| ease.ml/ci and ease.ml/meter in action: towards data management for statistical generalization C Renggli, FA Hubis, B Karlaš, K Schawinski, W Wu, C Zhang Proceedings of the VLDB Endowment 12 (12), 1962-1965, 2019 | 8 | 2019 |
| ease.ml in action: towards multi-tenant declarative learning services B Karlaš, J Liu, W Wu, C Zhang Proceedings of the VLDB Endowment 11 (12), 2054-2057, 2018 | 8 | 2018 |
| The curious case of the PDF converter that likes Mozart: Dissecting and mitigating the privacy risk of personal cloud apps H Harkous, R Rahman, B Karlaš, K Aberer Proceedings on Privacy Enhancing Technologies 2016 (4), 123-143, 2016 | 8 | 2016 |
| End-to-end Robustness for Sensing-Reasoning Machine Learning Pipelines Z Yang, Z Zhao, H Pei, B Wang, B Karlaš, J Liu, H Guo, B Li, C Zhang arXiv preprint arXiv:2003.00120, 2020 | 5 | 2020 |
| Automl from service provider’s perspective: Multi-device, multi-tenant model selection with gp-ei C Yu, B Karlaš, J Zhong, C Zhang, J Liu The 22nd International Conference on Artificial Intelligence and Statistics …, 2019 | 5* | 2019 |
| Screening Native ML Pipelines with “ArgusEyes” S Schelter, S Grafberger, S Guha, O Sprangers, B Karlaš, C Zhang 12th Annual Conference on Innovative Data Systems Research (CIDR 2022), 2022 | 3 | 2022 |
| Online Active Model Selection for Pre-trained Classifiers MR Karimi, NM Gürel, B Karlaš, J Rausch, C Zhang, A Krause International Conference on Artificial Intelligence and Statistics, 307-315, 2021 | 3 | 2021 |
| Data Debugging with Shapley Importance over End-to-End Machine Learning Pipelines B Karlaš, D Dao, M Interlandi, B Li, S Schelter, W Wu, C Zhang arXiv preprint arXiv:2204.11131, 2022 | 2 | 2022 |
| dcbench: a benchmark for data-centric AI systems S Eyuboglu, B Karlaš, C Ré, C Zhang, J Zou Proceedings of the Sixth Workshop on Data Management for End-To-End Machine …, 2022 | | 2022 |
Ce ZhangETH ZurichVerified email at inf.ethz.ch
