Joonas Jälkö

According to our database, Joonas Jälkö authored at least 18 papers between 2017 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Collaborative learning from distributed data with differentially private synthetic data.

Lukas Prediger, Joonas Jälkö, Antti Honkela, Samuel Kaski

BMC Medical Informatics Decis. Mak., December, 2024

Understanding Practical Membership Privacy of Deep Learning.

Marlon Tobaben, Gauri Pradhan, Yuan He, Joonas Jälkö, Antti Honkela

CoRR, 2024

Subsampling is not Magic: Why Large Batch Sizes Work for Differentially Private Stochastic Optimisation.

Ossi Räisä, Joonas Jälkö, Antti Honkela

Proceedings of the Forty-first International Conference on Machine Learning, 2024

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, Mario Fritz, Dimosthenis Karatzas

Proceedings of the Document Analysis and Recognition - ICDAR 2024 - 18th International Conference, Athens, Greece, August 30, 2024

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 Twin Data.

Lukas Prediger, Joonas Jälkö, Antti Honkela, Samuel Kaski

CoRR, 2023

Noise-Aware Statistical Inference with Differentially Private Synthetic Data.

Ossi Räisä, Joonas Jälkö, Samuel Kaski, Antti Honkela

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2021

Privacy-preserving data sharing via probabilistic modeling.

Joonas Jälkö, Eemil Lagerspetz, Jari Haukka, Sasu Tarkoma, Antti Honkela, Samuel Kaski

Patterns, 2021

Locally Differentially Private Bayesian Inference.

Tejas Kulkarni, Joonas Jälkö, Samuel Kaski, Antti Honkela

CoRR, 2021

Differentially Private Bayesian Inference for Generalized Linear Models.

Tejas Kulkarni, Joonas Jälkö, Antti Koskela, Samuel Kaski, Antti Honkela

Proceedings of the 38th International Conference on Machine Learning, 2021

Tight Differential Privacy for Discrete-Valued Mechanisms and for the Subsampled Gaussian Mechanism Using FFT.

Antti Koskela, Joonas Jälkö, Lukas Prediger, Antti Honkela

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020

Privacy-preserving Data Sharing on Vertically Partitioned Data.

Razane Tajeddine, Joonas Jälkö, Samuel Kaski, Antti Honkela

CoRR, 2020

Tight Approximate Differential Privacy for Discrete-Valued Mechanisms Using FFT.

Antti Koskela, Joonas Jälkö, Lukas Prediger, Antti Honkela

CoRR, 2020

Computing Tight Differential Privacy Guarantees Using FFT.

Antti Koskela, Joonas Jälkö, Antti Honkela

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019

Privacy-preserving data sharing via probabilistic modelling.

Joonas Jälkö, Eemil Lagerspetz, Jari Haukka, Sasu Tarkoma, Samuel Kaski, Antti Honkela

CoRR, 2019

Computing Exact Guarantees for Differential Privacy.

Antti Koskela, Joonas Jälkö, Antti Honkela

CoRR, 2019

Differentially Private Markov Chain Monte Carlo.

Mikko A. Heikkilä, Joonas Jälkö, Onur Dikmen, Antti Honkela

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

2017

Differentially Private Variational Inference for Non-conjugate Models.

Joonas Jälkö, Antti Honkela, Onur Dikmen

Proceedings of the Thirty-Third Conference on Uncertainty in Artificial Intelligence, 2017