| 2025 | AISTATS | Noise-Aware Differentially Private Variational Inference. | Talal Alrawajfeh, Joonas Jlk, Antti Honkela |
| 2024 | ICDAR | Privacy-Aware Document Visual Question Answering. | Rubn Tito, Khanh Nguyen, Marlon Tobaben, Raouf Kerkouche, Mohamed Ali Souibgui, Kangsoo Jung, Joonas Jlk, Vincent Poulain D'Andecy, Aurlie Joseph, Lei Kang, Ernest Valveny, Antti Honkela, Mario Fritz, Dimosthenis Karatzas |
| 2024 | ICML | Subsampling is not Magic: Why Large Batch Sizes Work for Differentially Private Stochastic Optimisation. | Ossi Ris, Joonas Jlk, Antti Honkela |
| 2023 | AISTATS | Noise-Aware Statistical Inference with Differentially Private Synthetic Data. | Ossi Ris, Joonas Jlk, Samuel Kaski, Antti Honkela |
| 2021 | AISTATS | Tight Differential Privacy for Discrete-Valued Mechanisms and for the Subsampled Gaussian Mechanism Using FFT. | Antti Koskela, Joonas Jlk, Lukas Prediger, Antti Honkela |
| 2021 | ICML | Differentially Private Bayesian Inference for Generalized Linear Models. | Tejas D. Kulkarni, Joonas Jlk, Antti Koskela, Samuel Kaski, Antti Honkela |
| 2020 | AISTATS | Computing Tight Differential Privacy Guarantees Using FFT. | Antti Koskela, Joonas Jlk, Antti Honkela |
| 2017 | UAI | Differentially Private Variational Inference for Non-conjugate Models. | Joonas Jlk, Antti Honkela, Onur Dikmen |