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

Provable Robust Learning Based on Transformation-Specific Smoothing. - Linyi Li 001; Maurice Weber; Xiaojun Xu; Luka Rimanic; Tao Xie 001; Ce Zhang 001; Bo Li 026 (CoRR, 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)

TSS: Transformation-Specific Smoothing for Robustness Certification. - Linyi Li 001; Maurice Weber; Xiaojun Xu; Luka Rimanic; Bhavya Kailkhura; Tao Xie 001; Ce Zhang 001; Bo Li 026 (CCS, 2021)

DataLens: Scalable Privacy Preserving Training via Gradient Compression and Aggregation. - Boxin Wang; Fan Wu 011; Yunhui Long; Luka Rimanic; Ce Zhang 001; Bo Li 026 (CCS, 2021)

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)

Knowledge Enhanced Machine Learning Pipeline against Diverse Adversarial Attacks. - Nezihe Merve Gürel; Xiangyu Qi; Luka Rimanic; Ce Zhang 001; Bo Li 026 (ICML, 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)

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)

Fine-tuning Language Models over Slow Networks using Activation Quantization with Guarantees. - Jue Wang; Binhang Yuan; Luka Rimanic; Yongjun He 004; Tri Dao; Beidi Chen; Christopher Ré; Ce Zhang 001 (NeurIPS, 2022)

Fine-tuning Language Models over Slow Networks using Activation Compression with Guarantees. - Jue Wang; Binhang Yuan; Luka Rimanic; Yongjun He 004; Tri Dao; Beidi Chen; Christopher Ré; 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)

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)

What Types of Automated Tests do Developers Write? - Marko Ivankovic; Luka Rimanic; Ivan Budiselic; Goran Petrovic; Gordon Fraser 001; René Just (AST@ICSE, 2025)