The Convergence of Sparsified Gradient Methods. - Dan Alistarh; Torsten Hoefler; Mikael Johansson 001; Nikola Konstantinov; Sarit Khirirat; Cédric Renggli (NeurIPS, 2018)

SparCML: High-Performance Sparse Communication for Machine Learning. - Cédric Renggli; Dan Alistarh; Torsten Hoefler (CoRR, 2018)

Distributed Learning over Unreliable Networks. - Chen Yu 009; Hanlin Tang; Cédric Renggli; Simon Kassing; Ankit Singla; Dan Alistarh; Ce Zhang 001; Ji Liu 002 (ICML, 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 001; Ce Zhang 001 (SysML, 2019)

SparCML: high-performance sparse communication for machine learning. - Cédric Renggli; Saleh Ashkboos; Mehdi Aghagolzadeh; Dan Alistarh; Torsten Hoefler (SC, 2019)

Lossy Image Compression with Recurrent Neural Networks: from Human Perceived Visual Quality to Classification Accuracy. - Maurice Weber; Cédric Renggli; Helmut Grabner; Ce Zhang 001 (CoRR, 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 001; Ce Zhang 001 (Proc. VLDB Endow., 2019)

Observer Dependent Lossy Image Compression. - Maurice Weber; Cédric Renggli; Helmut Grabner; Ce Zhang 001 (GCPR, 2020)

Building Continuous Integration Services for Machine Learning. - Bojan Karlas; Matteo Interlandi; Cédric Renggli; Wentao Wu 001; Ce Zhang 001; Deepak Mukunthu Iyappan Babu; Jordan Edwards; Chris Lauren; Andy Xu; Markus Weimer (KDD, 2020)

On Convergence of Nearest Neighbor Classifiers over Feature Transformations. - Luka Rimanic; Cédric Renggli; Bo Li 026; Ce Zhang 001 (NeurIPS, 2020)

On Automatic Feasibility Study for Machine Learning Application Development with ease.ml/snoopy. - Cédric Renggli; Luka Rimanic; Luka Kolar; Nora Hollenstein; Wentao Wu 001; Ce Zhang 001 (CoRR, 2020)

Ease.ml/snoopy in Action: Towards Automatic Feasibility Analysis for Machine Learning Application Development. - Cédric Renggli; Luka Rimanic; Luka Kolar; Wentao Wu 001; Ce Zhang 001 (Proc. VLDB Endow., 2020)

Ease.ML: A Lifecycle Management System for Machine Learning. - Leonel Aguilar Melgar; David Dao; Shaoduo Gan; Nezihe Merve Gürel; Nora Hollenstein; Jiawei Jiang 001; Bojan Karlas; Thomas Lemmin; Tian Li 005; Yang Li 106; Susie Xi Rao; Johannes Rausch; et al. (CIDR, 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 (ICLR, 2021)

Evaluating Bayes Error Estimators on Real-World Datasets with FeeBee. - Cédric Renggli; Luka Rimanic; Nora Hollenstein; Ce Zhang 001 (NeurIPS Datasets and Benchmarks, 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 001 (CoRR, 2021)

Evaluating Bayes Error Estimators on Read-World Datasets with FeeBee. - Cédric Renggli; Luka Rimanic; Nora Hollenstein; Ce Zhang 001 (CoRR, 2021)

A Data Quality-Driven View of MLOps. - Cédric Renggli; Luka Rimanic; Nezihe Merve Gürel; Bojan Karlas; Wentao Wu 001; Ce Zhang 001 (IEEE Data Eng. Bull., 2021)

Which Model to Transfer? Finding the Needle in the Growing Haystack. - Cédric Renggli; André Susano Pinto; Luka Rimanic; Joan Puigcerver; Carlos Riquelme; Ce Zhang 001; Mario Lucic (CVPR, 2022)

Dynamic Human Evaluation for Relative Model Comparisons. - Thórhildur Thorleiksdóttir; Cédric Renggli; Nora Hollenstein; Ce Zhang 001 (LREC, 2022)

In-Database Machine Learning with CorgiPile: Stochastic Gradient Descent without Full Data Shuffle. - Lijie Xu; Shuang Qiu; Binhang Yuan; Jiawei Jiang 001; Cédric Renggli; Shaoduo Gan; Kaan Kara; Guoliang Li 001; Ji Liu 002; Wentao Wu 001; Jieping Ye; Ce Zhang 001 (SIGMOD Conference, 2022)

Learning to Merge Tokens in Vision Transformers. - Cédric Renggli; André Susano Pinto; Neil Houlsby; Basil Mustafa; Joan Puigcerver; Carlos Riquelme (CoRR, 2022)

Stochastic Gradient Descent without Full Data Shuffle. - Lijie Xu; Shuang Qiu; Binhang Yuan; Jiawei Jiang 001; Cédric Renggli; Shaoduo Gan; Kaan Kara; Guoliang Li 001; Ji Liu 002; Wentao Wu 001; Jieping Ye; Ce Zhang 001 (CoRR, 2022)

SHiFT: An Efficient, Flexible Search Engine for Transfer Learning. - Cédric Renggli; Xiaozhe Yao; Luka Kolar; Luka Rimanic; Ana Klimovic; Ce Zhang 001 (Proc. VLDB Endow., 2022)

Building Data-Centric Systems for Machine Learning Development and Operations. - Cédric Renggli (2022)

Automatic Feasibility Study via Data Quality Analysis for ML: A Case-Study on Label Noise. - Cédric Renggli; Luka Rimanic; Luka Kolar; Wentao Wu 001; Ce Zhang 001 (ICDE, 2023)

Co-design Hardware and Algorithm for Vector Search. - Wenqi Jiang 001; Shigang Li 002; Yu Zhu; Johannes de Fine Licht; Zhenhao He; Runbin Shi; Cédric Renggli; Shuai Zhang 007; Theodoros Rekatsinas; Torsten Hoefler; Gustavo Alonso (SC, 2023)

DeepSE-WF: Unified Security Estimation for Website Fingerprinting Defenses. - Alexander Veicht; Cédric Renggli; Diogo Barradas (Proc. Priv. Enhancing Technol., 2023)

Incremental IVF Index Maintenance for Streaming Vector Search. - Jason Mohoney; Anil Pacaci; Shihabur Rahman Chowdhury; Umar Farooq Minhas; Jeffrey Pound; Cédric Renggli; Nima Reyhani; Ihab F. Ilyas; Theodoros Rekatsinas; Shivaram Venkataraman (CoRR, 2024)

Stochastic gradient descent without full data shuffle: with applications to in-database machine learning and deep learning systems. - Lijie Xu; Shuang Qiu; Binhang Yuan; Jiawei Jiang 001; Cédric Renggli; Shaoduo Gan; Kaan Kara; Guoliang Li 001; Ji Liu 002; Wentao Wu 001; Jieping Ye; Ce Zhang 001 (VLDB J., 2024)

Fundamental Challenges in Evaluating Text2SQL Solutions and Detecting Their Limitations. - Cédric Renggli; Ihab F. Ilyas; Theodoros Rekatsinas (CoRR, 2025)