Honkela, Antti

35 publications

AISTATS 2025 A Bias-Variance Decomposition for Ensembles over Multiple Synthetic Datasets Ossi Räisä, Antti Honkela

TMLR 2025 Empirical Comparison of Membership Inference Attacks in Deep Transfer Learning Yuxuan Bai, Gauri Pradhan, Marlon Tobaben, Antti Honkela

NeurIPS 2025 Impact of Dataset Properties on Membership Inference Vulnerability of Deep Transfer Learning Marlon Tobaben, Hibiki Ito, Joonas Jälkö, Yuan He, Antti Honkela

TMLR 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, Kevin Kuo, Virginia Smith, Yusuke Yamasaki, Takumi Fukami, Kenta Niwa, Iifan Tyou, Hiro Ishii, Rio Yokota, Ragul N, Rintu Kutum, Josep Llados, Ernest Valveny, Antti Honkela, Mario Fritz, Dimosthenis Karatzas

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

JMLR 2025 On Consistent Bayesian Inference from Synthetic Data Ossi Räisä, Joonas Jälkö, Antti Honkela

NeurIPSW 2024 Differentially Private Continual Learning Using Pre-Trained Models Marlon Tobaben, Marcus Klasson, Rui Li, Arno Solin, Antti Honkela

NeurIPS 2024 Noise-Aware Differentially Private Regression via Meta-Learning Ossi Räisä, Stratis Markou, Matthew Ashman, Wessel P. Bruinsma, Marlon Tobaben, Antti Honkela, Richard E. Turner

ICLRW 2024 Subsampling Is Not Magic: Why Large Batch Sizes Work for Differentially Private Stochastic Optimisation Ossi Räisä, Joonas Jälkö, Antti Honkela

ICML 2024 Subsampling Is Not Magic: Why Large Batch Sizes Work for Differentially Private Stochastic Optimisation Ossi Räisä, Joonas Jälkö, Antti Honkela

ICMLW 2024 Towards Efficient and Scalable Training of Differentially Private Deep Learning Sebastian Rodriguez Beltran, Marlon Tobaben, Niki Andreas Loppi, Antti Honkela

ICLRW 2024 Understanding Practical Membership Privacy of Deep Learning Marlon Tobaben, Gauri Pradhan, Yuan He, Joonas Jälkö, Antti Honkela

TMLR 2023 DPVIm: Differentially Private Variational Inference Improved Joonas Jälkö, Lukas Prediger, Antti Honkela, Samuel Kaski

ICLRW 2023 Differentially Private Federated Few-Shot Image Classification Aliaksandra Shysheya, Marlon Tobaben, John F Bronskill, Andrew Paverd, Shruti Tople, Santiago Zanella-Beguelin, Richard E Turner, Antti Honkela

TMLR 2023 Differentially Private Partitioned Variational Inference Mikko A. Heikkilä, Matthew Ashman, Siddharth Swaroop, Richard E Turner, Antti Honkela

ICLR 2023 Individual Privacy Accounting with Gaussian Differential Privacy Antti Koskela, Marlon Tobaben, Antti Honkela

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

TMLR 2023 Numerical Accounting in the Shuffle Model of Differential Privacy Antti Koskela, Mikko A. Heikkilä, Antti Honkela

NeurIPSW 2023 On Consistent Bayesian Inference from Synthetic Data Ossi Räisä, Joonas Jälkö, Antti Honkela

TMLR 2023 On the Efficacy of Differentially Private Few-Shot Image Classification Marlon Tobaben, Aliaksandra Shysheya, John F Bronskill, Andrew Paverd, Shruti Tople, Santiago Zanella-Beguelin, Richard E Turner, Antti Honkela

ICLRW 2023 On the Efficacy of Differentially Private Few-Shot Image Classification Marlon Tobaben, Aliaksandra Shysheya, John F Bronskill, Andrew Paverd, Shruti Tople, Santiago Zanella-Beguelin, Richard E Turner, Antti Honkela

NeurIPSW 2022 Individual Privacy Accounting with Gaussian Differential Privacy Antti Koskela, Marlon Tobaben, Antti Honkela

NeurIPSW 2022 Noise-Aware Statistical Inference with Differentially Private Synthetic Data Ossi Räisä, Joonas Jälkö, Samuel Kaski, Antti Honkela

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

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

NeurIPSW 2021 Differentially Private Hamiltonian Monte Carlo Ossi Räisä, Antti Koskela, Antti Honkela

NeurIPSW 2021 Tight Accounting in the Shuffle Model of Differential Privacy Antti Koskela, Mikko A. Heikkilä, Antti Honkela

AISTATS 2020 Computing Tight Differential Privacy Guarantees Using FFT Antti Koskela, Joonas Jälkö, Antti Honkela

AISTATS 2020 Learning Rate Adaptation for Differentially Private Learning Antti Koskela, Antti Honkela

NeurIPS 2019 Differentially Private Markov Chain Monte Carlo Mikko Heikkilä, Joonas Jälkö, Onur Dikmen, Antti Honkela

NeurIPS 2017 Differentially Private Bayesian Learning on Distributed Data Mikko Heikkilä, Eemil Lagerspetz, Samuel Kaski, Kana Shimizu, Sasu Tarkoma, Antti Honkela

UAI 2017 Differentially Private Variational Inference for Non-Conjugate Models Joonas Jälkö, Antti Honkela, Onur Dikmen

JMLR 2010 Approximate Riemannian Conjugate Gradient Learning for Fixed-Form Variational Bayes Antti Honkela, Tapani Raiko, Mikael Kuusela, Matti Tornio, Juha Karhunen

UAI 2005 Bayes Blocks: An Implementation of the Variational Bayesian Building Blocks Framework Markus Harva, Tapani Raiko, Antti Honkela, Harri Valpola, Juha Karhunen

NeurIPS 2004 Unsupervised Variational Bayesian Learning of Nonlinear Models Antti Honkela, Harri Valpola