Cédric Renggli

42 papers A* 7A 3B 1Journal 26Unranked 4

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	Fundamental Challenges in Evaluating Text2SQL Solutions and Detecting Their Limitations. CoRR Cédric Renggli, Ihab F. Ilyas, Theodoros Rekatsinas
2024	J	`jnl`	Incremental IVF Index Maintenance for Streaming Vector Search. CoRR Jason Mohoney, Anil Pacaci, Shihabur Rahman Chowdhury, Umar Farooq Minhas, Jeffrey Pound, Cédric Renggli, Nima Reyhani, Ihab F. Ilyas, Theodoros Rekatsinas, Shivaram Venkataraman
2024	J	`jnl`	Stochastic gradient descent without full data shuffle: with applications to in-database machine learning and deep learning systems. VLDB J. Lijie Xu, Shuang Qiu, Binhang Yuan, Jiawei Jiang, Cédric Renggli, Shaoduo Gan, Kaan Kara, Guoliang Li, Ji Liu, Wentao Wu, Jieping Ye, Ce Zhang
2023	A*	`conf`	Automatic Feasibility Study via Data Quality Analysis for ML: A Case-Study on Label Noise. ICDE Cédric Renggli, Luka Rimanic, Luka Kolar, Wentao Wu, Ce Zhang
2023	A	`conf`	Co-design Hardware and Algorithm for Vector Search. SC Wenqi Jiang, Shigang Li, Yu Zhu, Johannes de Fine Licht, Zhenhao He, Runbin Shi, Cédric Renggli, Shuai Zhang, Theodoros Rekatsinas, Torsten Hoefler, Gustavo Alonso
2023	J	`jnl`	Co-design Hardware and Algorithm for Vector Search. CoRR Wenqi Jiang, Shigang Li, Yu Zhu, Johannes de Fine Licht, Zhenhao He, Runbin Shi, Cédric Renggli, Shuai Zhang, Theodoros Rekatsinas, Torsten Hoefler, Gustavo Alonso
2023	J	`jnl`	DeepSE-WF: Unified Security Estimation for Website Fingerprinting Defenses. Proc. Priv. Enhancing Technol. Alexander Veicht, Cédric Renggli, Diogo Barradas
2022	—	`—`	Building Data-Centric Systems for Machine Learning Development and Operations. Cédric Renggli
2022	J	`jnl`	DeepSE-WF: Unified Security Estimation for Website Fingerprinting Defenses. CoRR Alexander Veicht, Cédric Renggli, Diogo Barradas
2022	B	`conf`	Dynamic Human Evaluation for Relative Model Comparisons. LREC Thórhildur Thorleiksdóttir, Cédric Renggli, Nora Hollenstein, Ce Zhang
2022	—	`conf`	In-Database Machine Learning with CorgiPile: Stochastic Gradient Descent without Full Data Shuffle. SIGMOD Conference Lijie Xu, Shuang Qiu, Binhang Yuan, Jiawei Jiang, Cédric Renggli, Shaoduo Gan, Kaan Kara, Guoliang Li, Ji Liu, Wentao Wu, Jieping Ye, Ce Zhang
2022	J	`jnl`	Learning to Merge Tokens in Vision Transformers. CoRR Cédric Renggli, André Susano Pinto, Neil Houlsby, Basil Mustafa, Joan Puigcerver, Carlos Riquelme
2022	J	`jnl`	SHiFT: An Efficient, Flexible Search Engine for Transfer Learning. CoRR Cédric Renggli, Xiaozhe Yao, Luka Kolar, Luka Rimanic, Ana Klimovic, Ce Zhang
2022	J	`jnl`	SHiFT: An Efficient, Flexible Search Engine for Transfer Learning. Proc. VLDB Endow. Cédric Renggli, Xiaozhe Yao, Luka Kolar, Luka Rimanic, Ana Klimovic, Ce Zhang
2022	J	`jnl`	Stochastic Gradient Descent without Full Data Shuffle. CoRR Lijie Xu, Shuang Qiu, Binhang Yuan, Jiawei Jiang, Cédric Renggli, Shaoduo Gan, Kaan Kara, Guoliang Li, Ji Liu, Wentao Wu, Jieping Ye, Ce Zhang
2022	A*	`conf`	Which Model to Transfer? Finding the Needle in the Growing Haystack. CVPR Cédric Renggli, André Susano Pinto, Luka Rimanic, Joan Puigcerver, Carlos Riquelme, Ce Zhang, Mario Lucic
2021	J	`jnl`	A Data Quality-Driven View of MLOps. CoRR Cédric Renggli, Luka Rimanic, Nezihe Merve Gürel, Bojan Karlas, Wentao Wu, Ce Zhang
2021	J	`jnl`	A Data Quality-Driven View of MLOps. IEEE Data Eng. Bull. Cédric Renggli, Luka Rimanic, Nezihe Merve Gürel, Bojan Karlas, Wentao Wu, Ce Zhang
2021	J	`jnl`	Decoding EEG Brain Activity for Multi-Modal Natural Language Processing. CoRR Nora Hollenstein, Cédric Renggli, Benjamin Glaus, Maria Barrett, Marius Troendle, Nicolas Langer, Ce Zhang
2021	J	`jnl`	Dynamic Human Evaluation for Relative Model Comparisons. CoRR Thórhildur Thorleiksdóttir, Cédric Renggli, Nora Hollenstein, Ce Zhang
2021	A	`conf`	Ease.ML: A Lifecycle Management System for Machine Learning. CIDR 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
2021	J	`jnl`	Evaluating Bayes Error Estimators on Read-World Datasets with FeeBee. CoRR Cédric Renggli, Luka Rimanic, Nora Hollenstein, Ce Zhang
2021	—	`conf`	Evaluating Bayes Error Estimators on Real-World Datasets with FeeBee. NeurIPS Datasets and Benchmarks Cédric Renggli, Luka Rimanic, Nora Hollenstein, Ce Zhang
2021	A*	`conf`	Scalable Transfer Learning with Expert Models. ICLR Joan Puigcerver, Carlos Riquelme Ruiz, Basil Mustafa, Cédric Renggli, André Susano Pinto, Sylvain Gelly, Daniel Keysers, Neil Houlsby
2020	A*	`conf`	Building Continuous Integration Services for Machine Learning. KDD Bojan Karlas, Matteo Interlandi, Cédric Renggli, Wentao Wu, Ce Zhang, Deepak Mukunthu Iyappan Babu, Jordan Edwards, Chris Lauren, Andy Xu, Markus Weimer
2020	J	`jnl`	Ease.ml/snoopy in Action: Towards Automatic Feasibility Analysis for Machine Learning Application Development. Proc. VLDB Endow. Cédric Renggli, Luka Rimanic, Luka Kolar, Wentao Wu, Ce Zhang
2020	—	`conf`	Observer Dependent Lossy Image Compression. GCPR Maurice Weber, Cédric Renggli, Helmut Grabner, Ce Zhang
2020	J	`jnl`	On Automatic Feasibility Study for Machine Learning Application Development with ease.ml/snoopy. CoRR Cédric Renggli, Luka Rimanic, Luka Kolar, Nora Hollenstein, Wentao Wu, Ce Zhang
2020	A*	`conf`	On Convergence of Nearest Neighbor Classifiers over Feature Transformations. NeurIPS Luka Rimanic, Cédric Renggli, Bo Li, Ce Zhang
2020	J	`jnl`	On Convergence of Nearest Neighbor Classifiers over Feature Transformations. CoRR Luka Rimanic, Cédric Renggli, Bo Li, Ce Zhang
2020	J	`jnl`	Scalable Transfer Learning with Expert Models. CoRR Joan Puigcerver, Carlos Riquelme, Basil Mustafa, Cédric Renggli, André Susano Pinto, Sylvain Gelly, Daniel Keysers, Neil Houlsby
2020	J	`jnl`	Which Model to Transfer? Finding the Needle in the Growing Haystack. CoRR Cédric Renggli, André Susano Pinto, Luka Rimanic, Joan Puigcerver, Carlos Riquelme, Ce Zhang, Mario Lucic
2019	—	`conf`	Continuous Integration of Machine Learning Models with ease.ml/ci: Towards a Rigorous Yet Practical Treatment. SysML Cédric Renggli, Bojan Karlas, Bolin Ding, Feng Liu, Kevin Schawinski, Wentao Wu, Ce Zhang
2019	J	`jnl`	Continuous Integration of Machine Learning Models with ease.ml/ci: Towards a Rigorous Yet Practical Treatment. CoRR Cédric Renggli, Bojan Karlas, Bolin Ding, Feng Liu, Kevin Schawinski, Wentao Wu, Ce Zhang
2019	A*	`conf`	Distributed Learning over Unreliable Networks. ICML Chen Yu, Hanlin Tang, Cédric Renggli, Simon Kassing, Ankit Singla, Dan Alistarh, Ce Zhang, Ji Liu
2019	J	`jnl`	Ease.ml/ci and Ease.ml/meter in Action: Towards Data Management for Statistical Generalization. Proc. VLDB Endow. Cédric Renggli, Frances Ann Hubis, Bojan Karlas, Kevin Schawinski, Wentao Wu, Ce Zhang
2019	J	`jnl`	Lossy Image Compression with Recurrent Neural Networks: from Human Perceived Visual Quality to Classification Accuracy. CoRR Maurice Weber, Cédric Renggli, Helmut Grabner, Ce Zhang
2019	A	`conf`	SparCML: high-performance sparse communication for machine learning. SC Cédric Renggli, Saleh Ashkboos, Mehdi Aghagolzadeh, Dan Alistarh, Torsten Hoefler
2018	J	`jnl`	Distributed Learning over Unreliable Networks. CoRR Hanlin Tang, Chen Yu, Cédric Renggli, Simon Kassing, Ankit Singla, Dan Alistarh, Ji Liu, Ce Zhang
2018	J	`jnl`	SparCML: High-Performance Sparse Communication for Machine Learning. CoRR Cédric Renggli, Dan Alistarh, Torsten Hoefler
2018	A*	`conf`	The Convergence of Sparsified Gradient Methods. NeurIPS Dan Alistarh, Torsten Hoefler, Mikael Johansson, Nikola Konstantinov, Sarit Khirirat, Cédric Renggli
2018	J	`jnl`	The Convergence of Sparsified Gradient Methods. CoRR Dan Alistarh, Torsten Hoefler, Mikael Johansson, Sarit Khirirat, Nikola Konstantinov, Cédric Renggli