Cédric Renggli

According to our database, Cédric Renggli authored at least 29 papers between 2018 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2023

DeepSE-WF: Unified Security Estimation for Website Fingerprinting Defenses.

Alexander Veicht, Cédric Renggli, Diogo Barradas

Proc. Priv. Enhancing Technol., April, 2023

Co-design Hardware and Algorithm for Vector Search.

Wenqi Jiang, Shigang Li, Yu Zhu, Johannes de Fine Licht, Zhenhao He, Runbin Shi, Cédric Renggli, Shuai Zhang, Theodoros Rekatsinas, Torsten Hoefler, Gustavo Alonso

CoRR, 2023

Co-design Hardware and Algorithm for Vector Search.

Wenqi Jiang, Shigang Li, Yu Zhu, Johannes de Fine Licht, Zhenhao He, Runbin Shi, Cédric Renggli, Shuai Zhang, Theodoros Rekatsinas, Torsten Hoefler, Gustavo Alonso

Proceedings of the International Conference for High Performance Computing, 2023

Automatic Feasibility Study via Data Quality Analysis for ML: A Case-Study on Label Noise.

Cédric Renggli, Luka Rimanic, Luka Kolar, Wentao Wu, Ce Zhang

Proceedings of the 39th IEEE International Conference on Data Engineering, 2023

2022

Building Data-Centric Systems for Machine Learning Development and Operations.

Cédric Renggli

PhD thesis, 2022

SHiFT: An Efficient, Flexible Search Engine for Transfer Learning.

Cédric Renggli, Xiaozhe Yao, Luka Kolar, Luka Rimanic, Ana Klimovic, Ce Zhang

Proc. VLDB Endow., 2022

Stochastic Gradient Descent without Full Data Shuffle.

Lijie Xu, Shuang Qiu, Binhang Yuan, Jiawei Jiang, Cédric Renggli, Shaoduo Gan, Kaan Kara, Guoliang Li, Ji Liu, Wentao Wu, Jieping Ye, Ce Zhang

CoRR, 2022

Learning to Merge Tokens in Vision Transformers.

Cédric Renggli, André Susano Pinto, Neil Houlsby, Basil Mustafa, Joan Puigcerver, Carlos Riquelme

CoRR, 2022

In-Database Machine Learning with CorgiPile: Stochastic Gradient Descent without Full Data Shuffle.

Lijie Xu, Shuang Qiu, Binhang Yuan, Jiawei Jiang, Cédric Renggli, Shaoduo Gan, Kaan Kara, Guoliang Li, Ji Liu, Wentao Wu, Jieping Ye, Ce Zhang

Proceedings of the SIGMOD '22: International Conference on Management of Data, Philadelphia, PA, USA, June 12, 2022

Dynamic Human Evaluation for Relative Model Comparisons.

Thórhildur Thorleiksdóttir, Cédric Renggli, Nora Hollenstein, Ce Zhang

Proceedings of the Thirteenth Language Resources and Evaluation Conference, 2022

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

Cédric Renggli, André Susano Pinto, Luka Rimanic, Joan Puigcerver, Carlos Riquelme, Ce Zhang, Mario Lucic

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022

2021

A Data Quality-Driven View of MLOps.

Cédric Renggli, Luka Rimanic, Nezihe Merve Gürel, Bojan Karlas, Wentao Wu, Ce Zhang

IEEE Data Eng. Bull., 2021

Evaluating Bayes Error Estimators on Read-World Datasets with FeeBee.

Cédric Renggli, Luka Rimanic, Nora Hollenstein, Ce Zhang

CoRR, 2021

Decoding EEG Brain Activity for Multi-Modal Natural Language Processing.

Nora Hollenstein, Cédric Renggli, Benjamin Glaus, Maria Barrett, Marius Troendle, Nicolas Langer, Ce Zhang

CoRR, 2021

Evaluating Bayes Error Estimators on Real-World Datasets with FeeBee.

Cédric Renggli, Luka Rimanic, Nora Hollenstein, Ce Zhang

Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1, 2021

Scalable Transfer Learning with Expert Models.

Joan Puigcerver, Carlos Riquelme Ruiz, Basil Mustafa, Cédric Renggli, André Susano Pinto, Sylvain Gelly, Daniel Keysers, Neil Houlsby

Proceedings of the 9th International Conference on Learning Representations, 2021

Ease.ML: A Lifecycle Management System for Machine Learning.

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

Proceedings of the 11th Conference on Innovative Data Systems Research, 2021

2020

Ease.ml/snoopy in Action: Towards Automatic Feasibility Analysis for Machine Learning Application Development.

Cédric Renggli, Luka Rimanic, Luka Kolar, Wentao Wu, Ce Zhang

Proc. VLDB Endow., 2020

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

Cédric Renggli, Luka Rimanic, Luka Kolar, Nora Hollenstein, Wentao Wu, Ce Zhang

CoRR, 2020

On Convergence of Nearest Neighbor Classifiers over Feature Transformations.

Luka Rimanic, Cédric Renggli, Bo Li, Ce Zhang

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Building Continuous Integration Services for Machine Learning.

Bojan Karlas, Matteo Interlandi, Cédric Renggli, Wentao Wu, Ce Zhang, Deepak Mukunthu Iyappan Babu, Jordan Edwards, Chris Lauren, Andy Xu, Markus Weimer

Proceedings of the KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2020

Observer Dependent Lossy Image Compression.

Maurice Weber, Cédric Renggli, Helmut Grabner, Ce Zhang

Proceedings of the Pattern Recognition - 42nd DAGM German Conference, DAGM GCPR 2020, Tübingen, Germany, September 28, 2020

2019

Ease.ml/ci and Ease.ml/meter in Action: Towards Data Management for Statistical Generalization.

Cédric Renggli, Frances Ann Hubis, Bojan Karlas, Kevin Schawinski, Wentao Wu, Ce Zhang

Proc. VLDB Endow., 2019

Lossy Image Compression with Recurrent Neural Networks: from Human Perceived Visual Quality to Classification Accuracy.

Maurice Weber, Cédric Renggli, Helmut Grabner, Ce Zhang

CoRR, 2019

SparCML: high-performance sparse communication for machine learning.

Cédric Renggli, Saleh Ashkboos, Mehdi Aghagolzadeh, Dan Alistarh, Torsten Hoefler

Proceedings of the International Conference for High Performance Computing, 2019

Continuous Integration of Machine Learning Models with ease.ml/ci: Towards a Rigorous Yet Practical Treatment.

Cédric Renggli, Bojan Karlas, Bolin Ding, Feng Liu, Kevin Schawinski, Wentao Wu, Ce Zhang

Proceedings of Machine Learning and Systems 2019, 2019

Distributed Learning over Unreliable Networks.

Chen Yu, Hanlin Tang, Cédric Renggli, Simon Kassing, Ankit Singla, Dan Alistarh, Ce Zhang, Ji Liu

Proceedings of the 36th International Conference on Machine Learning, 2019

2018

SparCML: High-Performance Sparse Communication for Machine Learning.

Cédric Renggli, Dan Alistarh, Torsten Hoefler

CoRR, 2018

The Convergence of Sparsified Gradient Methods.

Dan Alistarh, Torsten Hoefler, Mikael Johansson, Nikola Konstantinov, Sarit Khirirat, Cédric Renggli

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018