Summary of Training Differentially Private Ad Prediction Models with Semi-sensitive Features, by Lynn Chua et al.
Training Differentially Private Ad Prediction Models with Semi-Sensitive Featuresby Lynn Chua, Qiliang Cui, Badih Ghazi,…
Training Differentially Private Ad Prediction Models with Semi-Sensitive Featuresby Lynn Chua, Qiliang Cui, Badih Ghazi,…
Better Representations via Adversarial Training in Pre-Training: A Theoretical Perspectiveby Yue Xing, Xiaofeng Lin, Qifan…
Finite Sample Confidence Regions for Linear Regression Parameters Using Arbitrary Predictorsby Charles Guille-Escuret, Eugene NdiayeFirst…
GuardML: Efficient Privacy-Preserving Machine Learning Services Through Hybrid Homomorphic Encryptionby Eugene Frimpong, Khoa Nguyen, Mindaugas…
Adaptive Point Transformerby Alessandro Baiocchi, Indro Spinelli, Alessandro Nicolosi, Simone ScardapaneFirst submitted to arxiv on:…
Understanding Domain Generalization: A Noise Robustness Perspectiveby Rui Qiao, Bryan Kian Hsiang LowFirst submitted to…
Extracting Process-Aware Decision Models from Object-Centric Process Databy Alexandre Goossens, Johannes De Smedt, Jan VanthienenFirst…
Cross-Space Adaptive Filter: Integrating Graph Topology and Node Attributes for Alleviating the Over-smoothing Problemby Chen…
P3LS: Partial Least Squares under Privacy Preservationby Du Nguyen Duy, Ramin Nikzad-LangerodiFirst submitted to arxiv…
A structured regression approach for evaluating model performance across intersectional subgroupsby Christine Herlihy, Kimberly Truong,…