Antti Honkela

According to our database, Antti Honkela authored at least 73 papers between 2003 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

Differentially Private Continual Learning using Pre-Trained Models.

Marlon Tobaben, Marcus Klasson, Rui Li, Arno Solin, Antti Honkela

CoRR, 2024

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 Lladós, Ernest Valveny, Antti Honkela, Mario Fritz, Dimosthenis Karatzas

CoRR, 2024

Noise-Aware Differentially Private Variational Inference.

Talal Alrawajfeh, Joonas Jälkö, Antti Honkela

CoRR, 2024

Towards Efficient and Scalable Training of Differentially Private Deep Learning.

Sebastian Rodriguez Beltran, Marlon Tobaben, Niki Andreas Lopi, Antti Honkela

CoRR, 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

CoRR, 2024

Understanding Practical Membership Privacy of Deep Learning.

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

CoRR, 2024

A Bias-Variance Decomposition for Ensembles over Multiple Synthetic Datasets.

Ossi Räisä, 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

On the Efficacy of Differentially Private Few-shot Image Classification.

Marlon Tobaben, Aliaksandra Shysheya, John Bronskill, Andrew Paverd, Shruti Tople, Santiago Zanella Béguelin, Richard E. Turner, Antti Honkela

Trans. Mach. Learn. Res., 2023

Numerical Accounting in the Shuffle Model of Differential Privacy.

Antti Koskela, Mikko A. Heikkilä, Antti Honkela

Trans. Mach. Learn. Res., 2023

DPVIm: Differentially Private Variational Inference Improved.

Joonas Jälkö, Lukas Prediger, Antti Honkela, Samuel Kaski

Trans. Mach. Learn. Res., 2023

Differentially private partitioned variational inference.

Mikko A. Heikkilä, Matthew Ashman, Siddharth Swaroop, Richard E. Turner, Antti Honkela

Trans. Mach. Learn. Res., 2023

Privacy-Aware Document Visual Question Answering.

Rubèn Tito, Khanh Nguyen, Marlon Tobaben, Raouf Kerkouche, Mohamed Ali Souibgui, Kangsoo Jung, Lei Kang, Ernest Valveny, Antti Honkela, Mario Fritz, Dimosthenis Karatzas

CoRR, 2023

Collaborative Learning From Distributed Data With Differentially Private Synthetic Twin Data.

Lukas Prediger, Joonas Jälkö, Antti Honkela, Samuel Kaski

CoRR, 2023

Individual Privacy Accounting with Gaussian Differential Privacy.

Antti Koskela, Marlon Tobaben, Antti Honkela

Proceedings of the Eleventh International Conference on Learning Representations, 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

2022

d3p - A Python Package for Differentially-Private Probabilistic Programming.

Lukas Prediger, Niki Andreas Lopi, Samuel Kaski, Antti Honkela

Proc. Priv. Enhancing Technol., 2022

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 Hamiltonian Monte Carlo.

Ossi Räisä, Antti Koskela, Antti Honkela

CoRR, 2021

Tight Accounting in the Shuffle Model of Differential Privacy.

Antti Koskela, Mikko A. Heikkilä, Antti Honkela

CoRR, 2021

Gaussian Processes with Differential Privacy.

Antti Honkela

CoRR, 2021

Computing Differential Privacy Guarantees for Heterogeneous Compositions Using FFT.

Antti Koskela, 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

Differentially private cross-silo federated learning.

Mikko A. Heikkilä, Antti Koskela, Kana Shimizu, 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

Learning Rate Adaptation for Differentially Private Learning.

Antti Koskela, 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

Differentially Private Federated Variational Inference.

Mrinank Sharma, Michael J. Hutchinson, Siddharth Swaroop, Antti Honkela, Richard E. Turner

CoRR, 2019

Computing Exact Guarantees for Differential Privacy.

Antti Koskela, Joonas Jälkö, Antti Honkela

CoRR, 2019

Representation transfer for differentially private drug sensitivity prediction.

Teppo Mikael Niinimäki, Mikko A. Heikkilä, Antti Honkela, Samuel Kaski

Bioinform., 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

2018

Learning rate adaptation for differentially private stochastic gradient descent.

Antti Koskela, Antti Honkela

CoRR, 2018

2017

Differentially Private Bayesian Learning on Distributed Data.

Mikko A. Heikkilä, Yusuke Okimoto, Samuel Kaski, Kana Shimizu, Antti Honkela

CoRR, 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

Differentially private Bayesian learning on distributed data.

Mikko A. Heikkilä, Eemil Lagerspetz, Samuel Kaski, Kana Shimizu, Sasu Tarkoma, Antti Honkela

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

2016

Efficient differentially private learning improves drug sensitivity prediction.

Antti Honkela, Mrinal Das, Onur Dikmen, Samuel Kaski

CoRR, 2016

On the inconsistency of ℓ₁-penalised sparse precision matrix estimation.

Otte Heinävaara, Janne Leppä-aho, Jukka Corander, Antti Honkela

CoRR, 2016

On the inconsistency of ℓ 1-penalised sparse precision matrix estimation.

Otte Heinävaara, Janne Leppä-aho, Jukka Corander, Antti Honkela

BMC Bioinform., 2016

Analysis of differential splicing suggests different modes of short-term splicing regulation.

Hande Topa, Antti Honkela

Bioinform., 2016

2015

Gaussian process test for high-throughput sequencing time series: application to experimental evolution.

Hande Topa, Ágnes Jónás, Robert Kofler, Carolin Kosiol, Antti Honkela

Bioinform., 2015

Fast and accurate approximate inference of transcript expression from RNA-seq data.

James Hensman, Panagiotis Papastamoulis, Peter Glaus, Antti Honkela, Magnus Rattray

Bioinform., 2015

Gaussian process modelling of multiple short time series.

Hande Topa, Antti Honkela

Proceedings of the 23rd European Symposium on Artificial Neural Networks, 2015

2014

Inference of RNA Polymerase II Transcription Dynamics from Chromatin Immunoprecipitation Time Course Data.

Ciira Wa Maina, Antti Honkela, Filomena Matarese, Korbinian Grote, Hendrik G. Stunnenberg, George Reid, Neil D. Lawrence, Magnus Rattray

PLoS Comput. Biol., 2014

Exploration and retrieval of whole-metagenome sequencing samples.

Sohan Seth, Niko Välimäki, Samuel Kaski, Antti Honkela

Bioinform., 2014

2012

Identifying targets of multiple co-regulating transcription factors from expression time-series by Bayesian model comparison.

Michalis K. Titsias, Antti Honkela, Neil D. Lawrence, Magnus Rattray

BMC Syst. Biol., 2012

Identifying differentially expressed transcripts from RNA-seq data with biological variation.

Peter Glaus, Antti Honkela, Magnus Rattray

Bioinform., 2012

2011

Missing-Feature Reconstruction With a Bounded Nonlinear State-Space Model.

Ulpu Remes, Kalle J. Palomäki, Tapani Raiko, Antti Honkela, Mikko Kurimo

IEEE Signal Process. Lett., 2011

tigre: Transcription factor inference through gaussian process reconstruction of expression for bioconductor.

Antti Honkela, Pei Gao, Jonatan Ropponen, Magnus Rattray, Neil D. Lawrence

Bioinform., 2011

A Generative Approach for Image-Based Modeling of Tumor Growth.

Bjoern H. Menze, Koen Van Leemput, Antti Honkela, Ender Konukoglu, Marc-André Weber, Nicholas Ayache, Polina Golland

Proceedings of the Information Processing in Medical Imaging, 2011

2010

Model-based method for transcription factor target identification with limited data.

Antti Honkela, Charles Girardot, Eleanor Hilary Gustafson, Ya-Hsin Liu, Eileen E. M. Furlong, Neil D. Lawrence, Magnus Rattray

Proc. Natl. Acad. Sci. USA, 2010

Approximate Riemannian Conjugate Gradient Learning for Fixed-Form Variational Bayes.

Antti Honkela, Tapani Raiko, Mikael Kuusela, Matti Tornio, Juha Karhunen

J. Mach. Learn. Res., 2010

2009

A gradient-based algorithm competitive with variational Bayesian EM for mixture of Gaussians.

Mikael Kuusela, Tapani Raiko, Antti Honkela, Juha Karhunen

Proceedings of the International Joint Conference on Neural Networks, 2009

2008

Agglomerative independent variable group analysis.

Antti Honkela, Jeremias Seppä, Esa Alhoniemi

Neurocomputing, 2008

Gaussian process modelling of latent chemical species: applications to inferring transcription factor activities.

Pei Gao, Antti Honkela, Magnus Rattray, Neil D. Lawrence

Proceedings of the ECCB'08 Proceedings, 2008

2007

Compact Modeling of Data Using Independent Variable Group Analysis.

Esa Alhoniemi, Antti Honkela, Krista Lagus, Santeri Jeremias Seppä, Paul Wagner, Harri Valpola

IEEE Trans. Neural Networks, 2007

Blind separation of nonlinear mixtures by variational Bayesian learning.

Antti Honkela, Harri Valpola, Alexander Ilin, Juha Karhunen

Digit. Signal Process., 2007

Natural Conjugate Gradient in Variational Inference.

Antti Honkela, Matti Tornio, Tapani Raiko, Juha Karhunen

Proceedings of the Neural Information Processing, 14th International Conference, 2007

2006

State Inference in Variational Bayesian Nonlinear State-Space Models.

Tapani Raiko, Matti Tornio, Antti Honkela, Juha Karhunen

Proceedings of the Independent Component Analysis and Blind Signal Separation, 2006

2005

Bayes Blocks: An Implementation of the Variational Bayesian Building Blocks Framework.

Markus Harva, Tapani Raiko, Antti Honkela, Harri Valpola, Juha Karhunen

Proceedings of the UAI '05, 2005

Empirical evidence of the linear nature of magnetoencephalograms.

Antti Honkela, Tomas Östman, Ricardo Vigário

Proceedings of the 13th European Symposium on Artificial Neural Networks, 2005

2004

Variational learning and bits-back coding: an information-theoretic view to Bayesian learning.

Antti Honkela, Harri Valpola

IEEE Trans. Neural Networks, 2004

Unsupervised Variational Bayesian Learning of Nonlinear Models.

Antti Honkela, Harri Valpola

Proceedings of the Advances in Neural Information Processing Systems 17 [Neural Information Processing Systems, 2004

Approximating nonlinear transformations of probability distributions for nonlinear independent component analysis.

Antti Honkela

Proceedings of the IEEE International Joint Conference on Neural Networks, 2004

Post-nonlinear Independent Component Analysis by Variational Bayesian Learning.

Alexander Ilin, Antti Honkela

Proceedings of the Independent Component Analysis and Blind Signal Separation, 2004

Using Kernel PCA for Initialisation of Variational Bayesian Nonlinear Blind Source Separation Method.

Antti Honkela, Stefan Harmeling, Leo Lundqvist, Harri Valpola

Proceedings of the Independent Component Analysis and Blind Signal Separation, 2004

2003

Accelerating Cyclic Update Algorithms for Parameter Estimation by Pattern Searches.

Antti Honkela, Harri Valpola, Juha Karhunen

Neural Process. Lett., 2003

Nonlinear Blind Source Separation by Variational Bayesian Learning.

Harri Valpola, Erkki Oja, Alexander Ilin, Antti Honkela, Juha Karhunen

IEICE Trans. Fundam. Electron. Commun. Comput. Sci., 2003