3 citations · 3 across the 3 of their papers we have counts for
3 papers
cs.LG2026
Learning from Anonymized and Incomplete Tabular Data
Lucas Lange, Adrian Böttinger, Victor Christen +3
User-driven privacy allows individuals to control whether and at what granularity their data is shared, leading to datasets that mix original, generalized, and missing values withi…
cs.DB2024
Efficient Model Repository for Entity Resolution: Construction, Search, and Integration
Victor Christen, Peter Christen
Entity resolution (ER) is a fundamental task in data integration that enables insights from heterogeneous data sources. The primary challenge of ER lies in classifying record pairs…
cs.LG2022★ 3 cited
Privacy in Practice: Private COVID-19 Detection in X-Ray Images (Extended Version)
Lucas Lange, Maja Schneider, Peter Christen +1
Machine learning (ML) can help fight pandemics like COVID-19 by enabling rapid screening of large volumes of images. To perform data analysis while maintaining patient privacy, we…