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ORCIDCédric Renggli
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2020 – today
- 2024
[j6]Lijie Xu
, Shuang Qiu, Binhang Yuan, Jiawei Jiang, Cédric Renggli, Shaoduo Gan, Kaan Kara, Guoliang Li, Ji Liu, Wentao Wu, Jieping Ye, Ce Zhang:
Stochastic gradient descent without full data shuffle: with applications to in-database machine learning and deep learning systems. VLDB J. 33(5): 1231-1255 (2024)- 2023
- [j5]Alexander Veicht, Cédric Renggli, Diogo Barradas:
DeepSE-WF: Unified Security Estimation for Website Fingerprinting Defenses. Proc. Priv. Enhancing Technol. 2023(2): 188-205 (2023) - [c15]Cédric Renggli, Luka Rimanic, Luka Kolar, Wentao Wu, Ce Zhang:
Automatic Feasibility Study via Data Quality Analysis for ML: A Case-Study on Label Noise. ICDE 2023: 218-231 - [c14]Wenqi Jiang, Shigang Li, Yu Zhu, Johannes de Fine Licht, Zhenhao He, Runbin Shi, Cédric Renggli, Shuai Zhang, Theodoros Rekatsinas, Torsten Hoefler, Gustavo Alonso:
Co-design Hardware and Algorithm for Vector Search. SC 2023: 87:1-87:15 - [i18]Wenqi Jiang, Shigang Li, Yu Zhu, Johannes de Fine Licht, Zhenhao He, Runbin Shi, Cédric Renggli, Shuai Zhang, Theodoros Rekatsinas, Torsten Hoefler, Gustavo Alonso:
Co-design Hardware and Algorithm for Vector Search. CoRR abs/2306.11182 (2023) - 2022
- [b1]Cédric Renggli:
Building Data-Centric Systems for Machine Learning Development and Operations. ETH Zurich, Zürich, Switzerland, 2022 - [j4]Cédric Renggli, Xiaozhe Yao, Luka Kolar, Luka Rimanic, Ana Klimovic, Ce Zhang:
SHiFT: An Efficient, Flexible Search Engine for Transfer Learning. Proc. VLDB Endow. 16(2): 304-316 (2022) - [c13]Cédric Renggli, André Susano Pinto, Luka Rimanic, Joan Puigcerver, Carlos Riquelme, Ce Zhang, Mario Lucic:
Which Model to Transfer? Finding the Needle in the Growing Haystack. CVPR 2022: 9195-9204 - [c12]Thórhildur Thorleiksdóttir, Cédric Renggli, Nora Hollenstein, Ce Zhang:
Dynamic Human Evaluation for Relative Model Comparisons. LREC 2022: 5946-5955 - [c11]Lijie Xu, Shuang Qiu, Binhang Yuan, Jiawei Jiang, Cédric Renggli, Shaoduo Gan, Kaan Kara, Guoliang Li, Ji Liu, Wentao Wu, Jieping Ye, Ce Zhang:
In-Database Machine Learning with CorgiPile: Stochastic Gradient Descent without Full Data Shuffle. SIGMOD Conference 2022: 1286-1300 - [i17]Cédric Renggli, André Susano Pinto, Neil Houlsby, Basil Mustafa, Joan Puigcerver, Carlos Riquelme:
Learning to Merge Tokens in Vision Transformers. CoRR abs/2202.12015 (2022) - [i16]Alexander Veicht, Cédric Renggli, Diogo Barradas:
DeepSE-WF: Unified Security Estimation for Website Fingerprinting Defenses. CoRR abs/2203.04428 (2022) - [i15]Cédric Renggli, Xiaozhe Yao, Luka Kolar, Luka Rimanic, Ana Klimovic, Ce Zhang:
SHiFT: An Efficient, Flexible Search Engine for Transfer Learning. CoRR abs/2204.01457 (2022) - [i14]Lijie Xu, Shuang Qiu, Binhang Yuan, Jiawei Jiang, Cédric Renggli, Shaoduo Gan, Kaan Kara, Guoliang Li, Ji Liu, Wentao Wu, Jieping Ye, Ce Zhang:
Stochastic Gradient Descent without Full Data Shuffle. CoRR abs/2206.05830 (2022) - 2021
- [j3]Cédric Renggli, Luka Rimanic, Nezihe Merve Gürel, Bojan Karlas, Wentao Wu, Ce Zhang:
A Data Quality-Driven View of MLOps. IEEE Data Eng. Bull. 44(1): 11-23 (2021) - [c10]Leonel Aguilar Melgar, David Dao, Shaoduo Gan, Nezihe Merve Gürel, Nora Hollenstein, Jiawei Jiang, Bojan Karlas, Thomas Lemmin, Tian Li, Yang Li, Susie Xi Rao, Johannes Rausch, Cédric Renggli, Luka Rimanic, Maurice Weber, Shuai Zhang, Zhikuan Zhao, Kevin Schawinski, Wentao Wu, Ce Zhang:
Ease.ML: A Lifecycle Management System for Machine Learning. CIDR 2021 - [c9]Joan Puigcerver, Carlos Riquelme Ruiz, Basil Mustafa, Cédric Renggli, André Susano Pinto, Sylvain Gelly, Daniel Keysers, Neil Houlsby:
Scalable Transfer Learning with Expert Models. ICLR 2021 - [c8]Cédric Renggli, Luka Rimanic, Nora Hollenstein, Ce Zhang:
Evaluating Bayes Error Estimators on Real-World Datasets with FeeBee. NeurIPS Datasets and Benchmarks 2021 - [i13]Cédric Renggli, Luka Rimanic, Nezihe Merve Gürel, Bojan Karlas, Wentao Wu, Ce Zhang:
A Data Quality-Driven View of MLOps. CoRR abs/2102.07750 (2021) - [i12]Nora Hollenstein, Cédric Renggli, Benjamin Glaus, Maria Barrett, Marius Troendle, Nicolas Langer, Ce Zhang:
Decoding EEG Brain Activity for Multi-Modal Natural Language Processing. CoRR abs/2102.08655 (2021) - [i11]Cédric Renggli, Luka Rimanic, Nora Hollenstein, Ce Zhang:
Evaluating Bayes Error Estimators on Read-World Datasets with FeeBee. CoRR abs/2108.13034 (2021) - [i10]Thórhildur Thorleiksdóttir, Cédric Renggli, Nora Hollenstein, Ce Zhang:
Dynamic Human Evaluation for Relative Model Comparisons. CoRR abs/2112.08048 (2021) - 2020
- [j2]Cédric Renggli, Luka Rimanic, Luka Kolar, Wentao Wu, Ce Zhang:
Ease.ml/snoopy in Action: Towards Automatic Feasibility Analysis for Machine Learning Application Development. Proc. VLDB Endow. 13(12): 2837-2840 (2020) - [c7]Maurice Weber, Cédric Renggli, Helmut Grabner, Ce Zhang:
Observer Dependent Lossy Image Compression. GCPR 2020: 130-144 - [c6]Bojan Karlas, Matteo Interlandi, Cédric Renggli, Wentao Wu, Ce Zhang, Deepak Mukunthu Iyappan Babu, Jordan Edwards, Chris Lauren, Andy Xu, Markus Weimer:
Building Continuous Integration Services for Machine Learning. KDD 2020: 2407-2415 - [c5]Luka Rimanic, Cédric Renggli, Bo Li, Ce Zhang:
On Convergence of Nearest Neighbor Classifiers over Feature Transformations. NeurIPS 2020 - [i9]Joan Puigcerver, Carlos Riquelme, Basil Mustafa, Cédric Renggli, André Susano Pinto, Sylvain Gelly, Daniel Keysers, Neil Houlsby:
Scalable Transfer Learning with Expert Models. CoRR abs/2009.13239 (2020) - [i8]Cédric Renggli, André Susano Pinto, Luka Rimanic, Joan Puigcerver, Carlos Riquelme, Ce Zhang, Mario Lucic:
Which Model to Transfer? Finding the Needle in the Growing Haystack. CoRR abs/2010.06402 (2020) - [i7]Luka Rimanic, Cédric Renggli, Bo Li, Ce Zhang:
On Convergence of Nearest Neighbor Classifiers over Feature Transformations. CoRR abs/2010.07765 (2020) - [i6]Cédric Renggli, Luka Rimanic, Luka Kolar, Nora Hollenstein, Wentao Wu, Ce Zhang:
On Automatic Feasibility Study for Machine Learning Application Development with ease.ml/snoopy. CoRR abs/2010.08410 (2020)
2010 – 2019
- 2019
- [j1]Cédric Renggli, Frances Ann Hubis, Bojan Karlas, Kevin Schawinski, Wentao Wu, Ce Zhang:
Ease.ml/ci and Ease.ml/meter in Action: Towards Data Management for Statistical Generalization. Proc. VLDB Endow. 12(12): 1962-1965 (2019) - [c4]Chen Yu, Hanlin Tang, Cédric Renggli, Simon Kassing, Ankit Singla, Dan Alistarh, Ce Zhang, Ji Liu:
Distributed Learning over Unreliable Networks. ICML 2019: 7202-7212 - [c3]Cédric Renggli, Bojan Karlas, Bolin Ding, Feng Liu, Kevin Schawinski, Wentao Wu, Ce Zhang:
Continuous Integration of Machine Learning Models with ease.ml/ci: Towards a Rigorous Yet Practical Treatment. SysML 2019 - [c2]Cédric Renggli, Saleh Ashkboos, Mehdi Aghagolzadeh, Dan Alistarh, Torsten Hoefler:
SparCML: high-performance sparse communication for machine learning. SC 2019: 11:1-11:15 - [i5]Cédric Renggli, Bojan Karlas, Bolin Ding, Feng Liu, Kevin Schawinski, Wentao Wu, Ce Zhang:
Continuous Integration of Machine Learning Models with ease.ml/ci: Towards a Rigorous Yet Practical Treatment. CoRR abs/1903.00278 (2019) - [i4]Maurice Weber, Cédric Renggli, Helmut Grabner, Ce Zhang:
Lossy Image Compression with Recurrent Neural Networks: from Human Perceived Visual Quality to Classification Accuracy. CoRR abs/1910.03472 (2019) - 2018
- [c1]Dan Alistarh, Torsten Hoefler, Mikael Johansson, Nikola Konstantinov, Sarit Khirirat, Cédric Renggli:
The Convergence of Sparsified Gradient Methods. NeurIPS 2018: 5977-5987 - [i3]Cédric Renggli, Dan Alistarh, Torsten Hoefler:
SparCML: High-Performance Sparse Communication for Machine Learning. CoRR abs/1802.08021 (2018) - [i2]Dan Alistarh, Torsten Hoefler, Mikael Johansson, Sarit Khirirat, Nikola Konstantinov, Cédric Renggli:
The Convergence of Sparsified Gradient Methods. CoRR abs/1809.10505 (2018) - [i1]Hanlin Tang, Chen Yu, Cédric Renggli, Simon Kassing, Ankit Singla, Dan Alistarh, Ji Liu, Ce Zhang:
Distributed Learning over Unreliable Networks. CoRR abs/1810.07766 (2018)
Coauthor Index
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