Domain-Invariant Regression Under Beer-Lambert's Law. - Ramin Nikzad-Langerodi; Werner Zellinger; Susanne Saminger-Platz; Bernhard Moser 001 (ICMLA, 2019)

Graph-based calibration transfer. - Ramin Nikzad-Langerodi; Florian Sobieczky (CoRR, 2020)

Domain adaptation for regression under Beer-Lambert's law. - Ramin Nikzad-Langerodi; Werner Zellinger; Susanne Saminger-Platz; Bernhard Alois Moser (Knowl. Based Syst., 2020)

Robust Generalized Fuzzy Systems Training From High-Dimensional Time-Series Data Using Local Structure Preserving PLS. - Edwin Lughofer; Ramin Nikzad-Langerodi (IEEE Trans. Fuzzy Syst., 2020)

Using Property Graphs to Segment Time-Series Data. - Aleksei Karetnikov; Tobias Rehberger; Christian Lettner; Johannes Himmelbauer; Ramin Nikzad-Langerodi; Günter Gsellmann; Susanne Nestelberger; Stefan Schützeneder (DEXA Workshops, 2022)

Opening the black-box of Neighbor Embedding with Hotelling's T2 statistic and Q-residuals. - Roman Josef Rainer; Michael Mayr; Johannes Himmelbauer; Ramin Nikzad-Langerodi (CoRR, 2022)

Towards federated multivariate statistical process control (FedMSPC). - Du Nguyen Duy; David Gabauer; Ramin Nikzad-Langerodi (CoRR, 2022)

Towards Vertical Privacy-Preserving Symbolic Regression via Secure Multiparty Computation. - Du Nguyen Duy; Michael Affenzeller; Ramin Nikzad-Langerodi (GECCO Companion, 2023)

Supervised and Penalized Baseline Correction. - Erik Andries; Ramin Nikzad-Langerodi (CoRR, 2023)

VFLBench: A Practical Benchmark for Vertical Federated Learning in Smart Manufacturing. - Du Nguyen Duy; Ramin Nikzad-Langerodi; Michael Affenzeller (EUROCAST (1), 2024)

P3LS: Partial Least Squares under Privacy Preservation. - Du Nguyen Duy; Ramin Nikzad-Langerodi (CoRR, 2024)

(ε, δ)-Differentially Private Partial Least Squares Regression. - Ramin Nikzad-Langerodi; Mohit Kumar; Du Nguyen Duy; Mahtab Alghasi (CoRR, 2024)