On the importance of monitoring and directing progress in AI. - Lukas Prediger (AI Matters, 2017)

Tight Approximate Differential Privacy for Discrete-Valued Mechanisms Using FFT. - Antti Koskela; Joonas Jälkö; Lukas Prediger; Antti Honkela (CoRR, 2020)

Tight Differential Privacy for Discrete-Valued Mechanisms and for the Subsampled Gaussian Mechanism Using FFT. - Antti Koskela; Joonas Jälkö; Lukas Prediger; Antti Honkela (AISTATS, 2021)

d3p - A Python Package for Differentially-Private Probabilistic Programming. - Lukas Prediger; Niki Andreas Lopi; Samuel Kaski; Antti Honkela (Proc. Priv. Enhancing Technol., 2022)

Collaborative Learning From Distributed Data With Differentially Private Synthetic Twin Data. - Lukas Prediger; Joonas Jälkö; Antti Honkela; Samuel Kaski (CoRR, 2023)

DPVIm: Differentially Private Variational Inference Improved. - Joonas Jälkö; Lukas Prediger; Antti Honkela; Samuel Kaski (Trans. Mach. Learn. Res., 2023)

Collaborative learning from distributed data with differentially private synthetic data. - Lukas Prediger; Joonas Jälkö; Antti Honkela; Samuel Kaski (BMC Medical Informatics Decis. Mak., 2024)

Privacy-Preserving Neural Processes for Probabilistic User Modeling. - Amir Sonee; Haripriya Harikumar; Alex Hämäläinen; Lukas Prediger; Samuel Kaski (UAI, 2025)