Differentially Private Variational Inference for Non-conjugate Models. - Joonas Jälkö; Antti Honkela; Onur Dikmen (UAI, 2017)

Differentially Private Markov Chain Monte Carlo. - Mikko A. Heikkilä; Joonas Jälkö; Onur Dikmen; Antti Honkela (NeurIPS, 2019)

Computing Exact Guarantees for Differential Privacy. - Antti Koskela; Joonas Jälkö; Antti Honkela (CoRR, 2019)

Privacy-preserving data sharing via probabilistic modelling. - Joonas Jälkö; Eemil Lagerspetz; Jari Haukka; Sasu Tarkoma; Samuel Kaski; Antti Honkela (CoRR, 2019)

Computing Tight Differential Privacy Guarantees Using FFT. - Antti Koskela; Joonas Jälkö; Antti Honkela (AISTATS, 2020)

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

Privacy-preserving Data Sharing on Vertically Partitioned Data. - Razane Tajeddine; Joonas Jälkö; Samuel Kaski; 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)

Differentially Private Bayesian Inference for Generalized Linear Models. - Tejas Kulkarni; Joonas Jälkö; Antti Koskela; Samuel Kaski; Antti Honkela (ICML, 2021)

Locally Differentially Private Bayesian Inference. - Tejas Kulkarni; Joonas Jälkö; Samuel Kaski; Antti Honkela (CoRR, 2021)

Privacy-preserving data sharing via probabilistic modeling. - Joonas Jälkö; Eemil Lagerspetz; Jari Haukka; Sasu Tarkoma; Antti Honkela; Samuel Kaski (Patterns, 2021)

Noise-Aware Statistical Inference with Differentially Private Synthetic Data. - Ossi Räisä; Joonas Jälkö; Samuel Kaski; Antti Honkela (AISTATS, 2023)

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)

Privacy-Aware Document Visual Question Answering. - Rubèn Tito; Khanh Nguyen; Marlon Tobaben; Raouf Kerkouche; Mohamed Ali Souibgui; Kangsoo Jung; Joonas Jälkö; Vincent Poulain D'Andecy; Aurélie Joseph; Lei Kang; Ernest Valveny; Antti Honkela; et al. (ICDAR (6), 2024)

Subsampling is not Magic: Why Large Batch Sizes Work for Differentially Private Stochastic Optimisation. - Ossi Räisä; Joonas Jälkö; Antti Honkela (ICML, 2024)

Understanding Practical Membership Privacy of Deep Learning. - Marlon Tobaben; Gauri Pradhan; Yuan He; Joonas Jälkö; Antti Honkela (CoRR, 2024)

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

Noise-Aware Differentially Private Variational Inference. - Talal Alrawajfeh; Joonas Jälkö; Antti Honkela (AISTATS, 2025)

Hyperparameters in Score-Based Membership Inference Attacks. - Gauri Pradhan; Joonas Jälkö; Marlon Tobaben; Antti Honkela (SaTML, 2025)

NeurIPS 2023 Competition: Privacy Preserving Federated Learning Document VQA. - Marlon Tobaben; Mohamed Ali Souibgui; Rubèn Tito; Khanh Nguyen; Raouf Kerkouche; Kangsoo Jung; Joonas Jälkö; Lei Kang; Andrey Barsky; Vincent Poulain D'Andecy; Aurélie Joseph; Aashiq Muhamed; et al. (Trans. Mach. Learn. Res., 2025)