1 citations · 1 across the 5 of their papers we have counts for
7 papers
-Differentially Private Partial Least Squares Regression
Ramin Nikzad-Langerodi, Mohit Kumar, Du Nguyen Duy +1
As data-privacy requirements are becoming increasingly stringent and statistical models based on sensitive data are being deployed and used more routinely, protecting data-privacy…
P3LS: Partial Least Squares under Privacy Preservation
Du Nguyen Duy, Ramin Nikzad-Langerodi
Modern manufacturing value chains require intelligent orchestration of processes across company borders in order to maximize profits while fostering social and environmental sustai…
Supervised and Penalized Baseline Correction
Erik Andries, Ramin Nikzad-Langerodi
Spectroscopic measurements can show distorted spectral shapes arising from a mixture of absorbing and scattering contributions. These distortions (or baselines) often manifest them…
Towards Vertical Privacy-Preserving Symbolic Regression via Secure Multiparty Computation
Du Nguyen Duy, Michael Affenzeller, Ramin-Nikzad Langerodi
Symbolic Regression is a powerful data-driven technique that searches for mathematical expressions that explain the relationship between input variables and a target of interest. D…
Towards federated multivariate statistical process control (FedMSPC)
Du Nguyen Duy, David Gabauer, Ramin Nikzad-Langerodi
The ongoing transition from a linear (produce-use-dispose) to a circular economy poses significant challenges to current state-of-the-art information and communication technologies…
Opening the black-box of Neighbor Embedding with Hotelling's T2 statistic and Q-residuals
Roman Josef Rainer, Michael Mayr, Johannes Himmelbauer +1
In contrast to classical techniques for exploratory analysis of high-dimensional data sets, such as principal component analysis (PCA), neighbor embedding (NE) techniques tend to b…