Cédric Renggli

Publication Activity (10 Years)

Years Active: 2018-2023
Publications (10 Years): 38

Top Topics

Nearest Neighbor Classifiers

Machine Learning

Top Venues

Proc. VLDB Endow.

Publications

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)
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)
Alexander Veicht, Cédric Renggli, Diogo Barradas
DeepSE-WF: Unified Security Estimation for Website Fingerprinting Defenses. Proc. Priv. Enhancing Technol. 2023 (2) (2023)
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 (2023)
Thórhildur Thorleiksdóttir, Cédric Renggli, Nora Hollenstein, Ce Zhang
Dynamic Human Evaluation for Relative Model Comparisons. LREC (2022)
Alexander Veicht, Cédric Renggli, Diogo Barradas
DeepSE-WF: Unified Security Estimation for Website Fingerprinting Defenses. CoRR (2022)
Cédric Renggli, André Susano Pinto, Neil Houlsby, Basil Mustafa, Joan Puigcerver, Carlos Riquelme
Learning to Merge Tokens in Vision Transformers. CoRR (2022)
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)
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) (2022)
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)
Cédric Renggli, Xiaozhe Yao, Luka Kolar, Luka Rimanic, Ana Klimovic, Ce Zhang
SHiFT: An Efficient, Flexible Search Engine for Transfer Learning. CoRR (2022)
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 (2022)
Cédric Renggli, Luka Rimanic, Nora Hollenstein, Ce Zhang
Evaluating Bayes Error Estimators on Read-World Datasets with FeeBee. CoRR (2021)
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 (2021)
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)
Cédric Renggli, Luka Rimanic, Nora Hollenstein, Ce Zhang
Evaluating Bayes Error Estimators on Real-World Datasets with FeeBee. NeurIPS Datasets and Benchmarks (2021)
Thórhildur Thorleiksdóttir, Cédric Renggli, Nora Hollenstein, Ce Zhang
Dynamic Human Evaluation for Relative Model Comparisons. CoRR (2021)
Cédric Renggli, Luka Rimanic, Nezihe Merve Gürel, Bojan Karlas, Wentao Wu, Ce Zhang
A Data Quality-Driven View of MLOps. CoRR (2021)
Leonel Aguilar Melgar, David Dao, Shaoduo Gan, Nezihe Merve Gürel, Nora Hollenstein, Jiawei Jiang, Bojan Karlas, Thomas Lemmin, Tian Li, Yang Li, 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)
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) (2021)
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 (2020)
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 (2020)
Joan Puigcerver, Carlos Riquelme, Basil Mustafa, Cédric Renggli, André Susano Pinto, Sylvain Gelly, Daniel Keysers, Neil Houlsby
Scalable Transfer Learning with Expert Models. CoRR (2020)
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)
Maurice Weber, Cédric Renggli, Helmut Grabner, Ce Zhang
Observer Dependent Lossy Image Compression. GCPR (2020)
Luka Rimanic, Cédric Renggli, Bo Li, Ce Zhang
On Convergence of Nearest Neighbor Classifiers over Feature Transformations. NeurIPS (2020)
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) (2020)
Luka Rimanic, Cédric Renggli, Bo Li, Ce Zhang
On Convergence of Nearest Neighbor Classifiers over Feature Transformations. CoRR (2020)
Maurice Weber, Cédric Renggli, Helmut Grabner, Ce Zhang
Lossy Image Compression with Recurrent Neural Networks: from Human Perceived Visual Quality to Classification Accuracy. CoRR (2019)
Chen Yu, Hanlin Tang, Cédric Renggli, Simon Kassing, Ankit Singla, Dan Alistarh, Ce Zhang, Ji Liu
Distributed Learning over Unreliable Networks. ICML (2019)
Cédric Renggli, Saleh Ashkboos, Mehdi Aghagolzadeh, Dan Alistarh, Torsten Hoefler
SparCML: high-performance sparse communication for machine learning. SC (2019)
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. MLSys (2019)
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) (2019)
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 (2019)
Dan Alistarh, Torsten Hoefler, Mikael Johansson, Nikola Konstantinov, Sarit Khirirat, Cédric Renggli
The Convergence of Sparsified Gradient Methods. NeurIPS (2018)
Cédric Renggli, Dan Alistarh, Torsten Hoefler
SparCML: High-Performance Sparse Communication for Machine Learning. CoRR (2018)
Hanlin Tang, Chen Yu, Cédric Renggli, Simon Kassing, Ankit Singla, Dan Alistarh, Ji Liu, Ce Zhang
Distributed Learning over Unreliable Networks. CoRR (2018)
Dan Alistarh, Torsten Hoefler, Mikael Johansson, Sarit Khirirat, Nikola Konstantinov, Cédric Renggli
The Convergence of Sparsified Gradient Methods. CoRR (2018)