Cedric Renggli



Cedric Renggli

I am a Senior Machine Learning Researcher at Apple. My main research interest lies in the foundation of scalable data systems, with a special focus on efficiently involving machine learning models.

Previously, I was a PostDoc in the Data Systems and Theory group lead by Dan Olteanu at UZH. I defended my thesis on Building Data-Centric Systems for Machine Learning Development and Operations at ETH Zurich's System Group under the supervision of Ce Zhang. I hold a bachelor's degree from the Bern University of Applied Sciences and received my MSc in Computer Science from ETH Zurich in 2018. My work on Efficient Sparse AllReduce For Scalable Machine Learning was awarded with the silver medal of ETH Zurich for outstanding master thesis. During my PhD, I have worked as a research intern and student research consultant at Google Brain.

Publications


For a up-to-date list of my publications check my profile on Google Scholar.

2024

Stochastic Gradient Descent without Full Data Shuffle: with Applications to In-Database Machine Learning and Deep Learning Systems
Lijie Xu, ..., Cedric Renggli, ..., Wentao Wu and Ce Zhang
The VLDB Journal 2024

2023

SHiFT: An Efficient, Flexible Search Engine for Transfer Learning
Cedric Renggli, Xiaozhe Yao, Luka Kolar, Luka Rimanic, Ana Klimovic and Ce Zhang
International Conference on Very Large Data Bases (VLDB) 2023
Automatic Feasibility Study via Data Quality Analysis for ML: A Case-Study on Label Noise
Cedric Renggli*, Luka Rimanic*, Luka Kolar*, Wentao Wu and Ce Zhang
IEEE International Conference on Data Engineering (ICDE) 2023
DeepSE-WF: Unified Security Estimation for Website Fingerprinting Defenses
Alexander Veicht, Cedric Renggli and Diogo Barradas
Privacy Enhancing Technologies Symposium (PETS) 2023
Co-design Hardware and Algorithm for Vector Search
Wenqi Jiang, ..., Cedric Renggli, ..., Torsten Hoefler and Gustavo Alonso
High Performance Computing, Networking, Storage and Analysis (SC) 2023

2022

Which Model to Transfer? Finding the Needle in the Growing Haystack
Cedric Renggli, Andre Susano Pinto, Luka Rimanic, Joan Puigcerver, Carlos Riquelme, Ce Zhang and Mario Lucic
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2022
Dynamic Human Evaluation for Relative Model Comparisons
Thórhildur Thorleiksdóttir, Cedric Renggli, Nora Hollenstein and Ce Zhang
Language Resources and Evaluation Conference (LREC) 2022
In-Database Machine Learning with CorgiPile: Stochastic Gradient Descent without Full Data Shuffle
Lijie Xu, ..., Cedric Renggli, ..., Wentao Wu and Ce Zhang
ACM Special Interest Group in Management Of Data (SIGMOD) 2022
Learning to Merge Tokens in Vision Transformers
Cedric Renggli*, Andre Susano Pinto*, Neil Houlsby, Basil Mustafa, Joan Puigcerver and Carlos Riquelme*
Sparsity in Neural Networks (SSN) Workshop, Honorable Mentions

2021

Evaluating Bayes Error Estimators on Real-World Datasets with FeeBee
Cedric Renggli*, Luka Rimanic*, Nora Hollenstein and Ce Zhang
Neural Information Processing Systems (NeurIPS) 2021, Datasets and Benchmarks
A Data Quality-Driven View of MLOps
Cedric Renggli, Luka Rimanic, Nezihe Merve Gürel, Bojan Karlaš, Wentao Wu and Ce Zhang
IEEE Data Engineering Bulletin March 2021
Scalable Transfer Learning with Expert Models
Joan Puigcerver, Carlos Riquelme, Basil Mustafa, Cedric Renggli, Andre Susano Pinto, Sylvain Gelly, Daniel Keysers and Neil Houlsby
International Conference on Learning Representations (ICLR) 2021
Ease.ML: A Lifecycle Management System for MLDev and MLOps
Aguilar Leonel, ..., Cedric Renggli, ..., Wentao Wu and Ce Zhang
Conference on Innovative Data Systems Research (CIDR) 2021
Decoding EEG Brain Activity for Multi-Modal Natural Language Processing
Nora Hollenstein, Cedric Renggli, Benjamin Glaus, Maria Barrett, Marius Troendle, Nicolas Langer and Ce Zhang
Frontiers in Human Neuroscience Vol. 15 2021

2020

On Convergence of Nearest Neighbor Classifiers over Feature Transformations
Luka Rimanic*, Cedric Renggli*, Bo Li and Ce Zhang
Neural Information Processing Systems (NeurIPS) 2020
Ease.ml/snoopy in Action: Towards Automatic Feasibility Analysis for Machine Learning Application Development
Cedric Renggli*, Luka Rimanic*, Luka Kolar, Wentao Wu and Ce Zhang
International Conference on Very Large Data Bases (VLDB) 2020, Demo
Observer Dependent Lossy Image Compression
Maurice Weber, Cedric Renggli, Helmut Grabner and Ce Zhang
German Conference on Pattern Recognition (DAGM-GCPR) 2020
Building Continuous Integration Services for Machine Learning
Bojan Karlaš, Matteo Interlandi, Cedric Renggli, Wentao Wu, Ce Zhang, Deepak Mukunthu Iyappan Babu, Jordan Edwards, Chris Lauren, Andy Xu and Markus Weimer
Special Interest Group on Knowledge Discovery and Data Mining (SIGKDD) 2020, Applied Data Science, Oral Presentation 44/756

2019

SparCML: High-Performance Sparse Communication for Machine Learning
Cedric Renggli, Saleh Ashkboos, Mehdi Aghagolzadeh, Dan Alistarh and Torsten Hoefler
High Performance Computing, Networking, Storage and Analysis (SC) 2019
Ease.ml/ci and Ease.ml/meter in Action: Towards Data Management for Statistical Generalization
Cedric Renggli*, Frances Ann Hubis*, Bojan Karlaš, Kevin Schawinski, Wentao Wu and Ce Zhang
International Conference on Very Large Data Bases (VLDB) 2019, Demo
Distributed Learning over Unreliable Networks
Chen Yu, Hanlin Tang, Cedric Renggli, Simon Kassing, Ankit Singla, Dan Alistarh, Ji Liu and Ce Zhang
International Conference on Machine Learning (ICML) 2019
Continuous Integration of Machine Learning Models with ease.ml/ci: Towards a Rigorous Yet Practical Treatment
Cedric Renggli, Bojan Karlaš, Bolin Ding, Feng Liu, Kevin Schawinski, Wentao Wu and Ce Zhang
Conference on Systems and Machine Learning (SysML) 2019
Speeding up Percolator
John T. Halloran, Hantian Zhang, Kaan Kara, Cedric Renggli, Matthew The, Ce Zhang, David M. Rocke, Lukas Käll and William Stafford Noble
Journal of Proteome Research 2019

2018

The Convergence of Sparsified Gradient Methods
Dan Alistarh, Torsten Hoefler, Mikael Johansson, Nikola Konstantinov, Sarit Khirirat and Cedric Renggli (Authors ordered alphabetically)
Neural Information Processing Systems (NeurIPS) 2018

2017

MPIML: A High-Performance Sparse Communication Layer for Machine Learning (Poster)
Cedric Renggli, Dan Alistarh and Torsten Hoefler
Neural Information Processing Systems (NIPS) 2017, Workshop: Deep Learning At Supercomputer Scale
 

PhD Thesis

Building Data-Centric Systems for Machine Learning Development and Operations
Cedric Renggli
 

MSc Thesis

Efficient Sparse AllReduce For Scalable Machine Learning
Cedric Renggli
Outstanding thesis award: Silver medal of ETH Zurich
 

Contact


 
Cedric Renggli
Apple Inc.