Luka Rimanic
Luka Rimanic
DS3 Lab, Systems Group, ETH Zurich
Verified email at - Homepage
Cited by
Cited by
On side lengths of corners in positive density subsets of the Euclidean space
P Durcik, V Kovač, L Rimanić
International Mathematics Research Notices 2018 (22), 6844-6869, 2018
Provable Robust Learning Based on Transformation-Specific Smoothing
L Li, M Weber, X Xu, L Rimanic, T Xie, C Zhang, B Li
arXiv preprint arXiv:2002.12398, 2020
Ease. ml/snoopy in action: towards automatic feasibility analysis for machine learning application development
C Renggli, L Rimanic, L Kolar, W Wu, C Zhang
Proceedings of the VLDB Endowment 13 (12), 2837-2840, 2020
A Data Quality-Driven View of MLOps
C Renggli, L Rimanic, NM Gürel, B Karlaš, W Wu, C Zhang
arXiv preprint arXiv:2102.07750, 2021
Ease. ML: A Lifecycle Management System for Machine Learning
L Aguilar Melgar, D Dao, S Gan, NM Gürel, N Hollenstein, J Jiang, ...
11th Annual Conference on Innovative Data Systems Research (CIDR 2021)(virtual), 2021
Which Model to Transfer? Finding the Needle in the Growing Haystack
C Renggli, AS Pinto, L Rimanic, J Puigcerver, C Riquelme, C Zhang, ...
arXiv preprint arXiv:2010.06402, 2020
Szemerédi's Theorem in the Primes
L Rimanić, J Wolf
Proceedings of the Edinburgh Mathematical Society 62 (2), 443-457, 2019
Lonely runners in function fields
S Chow, L Rimanić
Mathematika 65 (3), 677-701, 2019
TSS: Transformation-Specific Smoothing for Robustness Certification
L Li, M Weber, X Xu, L Rimanic, B Kailkhura, T Xie, C Zhang, B Li
CCS, 2021
On Automatic Feasibility Study for Machine Learning Application Development with ease. ml/snoopy
C Renggli, L Rimanic, L Kolar, N Hollenstein, W Wu, C Zhang
arXiv preprint arXiv:2010.08410, 2020
On Convergence of Nearest Neighbor Classifiers over Feature Transformations
L Rimanic, C Renggli, B Li, C Zhang
Advances in Neural Information Processing System 33, 2020
Knowledge Enhanced Machine Learning Pipeline against Diverse Adversarial Attacks
NM Gürel, X Qi, L Rimanic, C Zhang, B Li
arXiv preprint arXiv:2106.06235, 2021
DataLens: Scalable Privacy Preserving Training via Gradient Compression and Aggregation
B Wang, F Wu, Y Long, L Rimanic, C Zhang, B Li
arXiv preprint arXiv:2103.11109, 2021
Arithmetic progressions, corners and loneliness
L Rimanić
University of Bristol, 2018
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