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Evaluating Bayes Error Estimators on Read-World Datasets with FeeBee


Aug 30, 2021
Cedric Renggli, Luka Rimanic, Nora Hollenstein, Ce Zhang

* arXiv admin note: text overlap with arXiv:2010.08410 

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Knowledge Enhanced Machine Learning Pipeline against Diverse Adversarial Attacks


Jun 11, 2021
Nezihe Merve Gürel, Xiangyu Qi, Luka Rimanic, Ce Zhang, Bo Li

* International Conference on Machine Learning 2021, 37 pages, 8 figures, 9 tables 

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DataLens: Scalable Privacy Preserving Training via Gradient Compression and Aggregation


Mar 20, 2021
Boxin Wang, Fan Wu, Yunhui Long, Luka Rimanic, Ce Zhang, Bo Li

* 29 pages, 3 figures, 6 tables 

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A Data Quality-Driven View of MLOps


Feb 15, 2021
Cedric Renggli, Luka Rimanic, Nezihe Merve Gürel, Bojan Karlaš, Wentao Wu, Ce Zhang


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On Automatic Feasibility Study for Machine Learning Application Development with ease.ml/snoopy


Oct 16, 2020
Cedric Renggli, Luka Rimanic, Luka Kolar, Nora Hollenstein, Wentao Wu, Ce Zhang


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On Convergence of Nearest Neighbor Classifiers over Feature Transformations


Oct 15, 2020
Luka Rimanic, Cedric Renggli, Bo Li, Ce Zhang


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Which Model to Transfer? Finding the Needle in the Growing Haystack


Oct 13, 2020
Cedric Renggli, André Susano Pinto, Luka Rimanic, Joan Puigcerver, Carlos Riquelme, Ce Zhang, Mario Lucic


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Provable Robust Learning Based on Transformation-Specific Smoothing


Mar 20, 2020
Linyi Li, Maurice Weber, Xiaojun Xu, Luka Rimanic, Tao Xie, Ce Zhang, Bo Li

* Corrected typos in Appendix 

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