Bojan Karlaš
Bojan Karlaš
Потвърден имейл адрес: hms.harvard.edu - Начална страница
RAB: Provable Robustness Against Backdoor Attacks
M Weber, X Xu, B Karlaš, C Zhang, B Li
arXiv preprint arXiv:2003.08904, 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
A data quality-driven view of mlops
C Renggli, L Rimanic, NM Gürel, B Karlaš, W Wu, C Zhang
IEEE Data Engineering Bulletin, 2021
Dataperf: Benchmarks for data-centric ai development
M Mazumder, C Banbury, X Yao, B Karlaš, WG Rojas, S Diamos, ...
arXiv preprint arXiv:2207.10062, 2022
Data Science Through the Looking Glass: Analysis of Millions of GitHub Notebooks and ML .NET Pipelines
F Psallidas, Y Zhu, B Karlaš, J Henkel, M Interlandi, S Krishnan, B Kroth, ...
SIGMOD Record, 2022
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
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
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
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
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
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
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
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
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
Improving certified robustness via statistical learning with logical reasoning
Z Yang, Z Zhao, B Wang, J Zhang, L Li, H Pei, B Karlaš, J Liu, H Guo, ...
Advances in Neural Information Processing Systems 35, 34859-34873, 2022
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
Provable robustness against backdoor attacks
M Weber, X Xu, B Karlas, C Zhang, BL RAB
arXiv preprint arXiv:2003.08904, 2020
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
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
Proactively Screening Machine Learning Pipelines with ArgusEyes
S Schelter, S Grafberger, S Guha, B Karlas, C Zhang
Companion of the 2023 International Conference on Management of Data, 91-94, 2023
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