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)

Accelerating Cyclic Update Algorithms for Parameter Estimation by Pattern Searches. - Antti Honkela; Harri Valpola; Juha Karhunen (Neural Process. Lett., 2003)

Using Kernel PCA for Initialisation of Variational Bayesian Nonlinear Blind Source Separation Method. - Antti Honkela; Stefan Harmeling; Leo Lundqvist; Harri Valpola (ICA, 2004)

Post-nonlinear Independent Component Analysis by Variational Bayesian Learning. - Alexander Ilin; Antti Honkela (ICA, 2004)

Approximating nonlinear transformations of probability distributions for nonlinear independent component analysis. - Antti Honkela (IJCNN, 2004)

Unsupervised Variational Bayesian Learning of Nonlinear Models. - Antti Honkela; Harri Valpola (NIPS, 2004)

Variational learning and bits-back coding: an information-theoretic view to Bayesian learning. - Antti Honkela; Harri Valpola (IEEE Trans. Neural Networks, 2004)

Empirical evidence of the linear nature of magnetoencephalograms. - Antti Honkela; Tomas Östman; Ricardo Vigário (ESANN, 2005)

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

State Inference in Variational Bayesian Nonlinear State-Space Models. - Tapani Raiko; Matti Tornio; Antti Honkela; Juha Karhunen (ICA, 2006)

Natural Conjugate Gradient in Variational Inference. - Antti Honkela; Matti Tornio; Tapani Raiko; Juha Karhunen (ICONIP (2), 2007)

Blind separation of nonlinear mixtures by variational Bayesian learning. - Antti Honkela; Harri Valpola; Alexander Ilin; Juha Karhunen (Digit. Signal Process., 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)

Gaussian process modelling of latent chemical species: applications to inferring transcription factor activities. - Pei Gao; Antti Honkela; Magnus Rattray; Neil D. Lawrence (ECCB, 2008)

Agglomerative independent variable group analysis. - Antti Honkela; Jeremias Seppä; Esa Alhoniemi (Neurocomputing, 2008)

A gradient-based algorithm competitive with variational Bayesian EM for mixture of Gaussians. - Mikael Kuusela; Tapani Raiko; Antti Honkela; Juha Karhunen (IJCNN, 2009)

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)

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)

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 (IPMI, 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)

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)

Identifying differentially expressed transcripts from RNA-seq data with biological variation. - Peter Glaus; Antti Honkela; Magnus Rattray (Bioinform., 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)

Exploration and retrieval of whole-metagenome sequencing samples. - Sohan Seth; Niko Välimäki; Samuel Kaski; Antti Honkela (Bioinform., 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)

Gaussian process modelling of multiple short time series. - Hande Topa; Antti Honkela (ESANN, 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 test for high-throughput sequencing time series: application to experimental evolution. - Hande Topa; Ágnes Jónás; Robert Kofler; Carolin Kosiol; Antti Honkela (Bioinform., 2015)

Analysis of differential splicing suggests different modes of short-term splicing regulation. - Hande Topa; Antti Honkela (Bioinform., 2016)

On the inconsistency of ℓ 1-penalised sparse precision matrix estimation. - Otte Heinävaara; Janne Leppä-aho; Jukka Corander; Antti Honkela (BMC Bioinform., 2016)

On the inconsistency of ℓ1-penalised sparse precision matrix estimation. - Otte Heinävaara; Janne Leppä-aho; Jukka Corander; Antti Honkela (CoRR, 2016)

Efficient differentially private learning improves drug sensitivity prediction. - Antti Honkela; Mrinal Das; Onur Dikmen; Samuel Kaski (CoRR, 2016)

Differentially private Bayesian learning on distributed data. - Mikko A. Heikkilä; Eemil Lagerspetz; Samuel Kaski; Kana Shimizu; Sasu Tarkoma; Antti Honkela (NIPS, 2017)

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

Differentially Private Bayesian Learning on Distributed Data. - Mikko A. Heikkilä; Yusuke Okimoto; Samuel Kaski; Kana Shimizu; Antti Honkela (CoRR, 2017)

Learning rate adaptation for differentially private stochastic gradient descent. - Antti Koskela; Antti Honkela (CoRR, 2018)

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

Representation transfer for differentially private drug sensitivity prediction. - Teppo Mikael Niinimäki; Mikko A. Heikkilä; Antti Honkela; Samuel Kaski (Bioinform., 2019)

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

Differentially Private Federated Variational Inference. - Mrinank Sharma; Michael J. Hutchinson; Siddharth Swaroop; Antti Honkela; Richard E. Turner (CoRR, 2019)

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

Learning Rate Adaptation for Differentially Private Learning. - Antti Koskela; Antti Honkela (AISTATS, 2020)

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)

Differentially private cross-silo federated learning. - Mikko A. Heikkilä; Antti Koskela; Kana Shimizu; Samuel Kaski; 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)

Computing Differential Privacy Guarantees for Heterogeneous Compositions Using FFT. - Antti Koskela; Antti Honkela (CoRR, 2021)

Gaussian Processes with Differential Privacy. - Antti Honkela (CoRR, 2021)

Tight Accounting in the Shuffle Model of Differential Privacy. - Antti Koskela; Mikko A. Heikkilä; Antti Honkela (CoRR, 2021)

Differentially Private Hamiltonian Monte Carlo. - Ossi Räisä; Antti Koskela; Antti Honkela (CoRR, 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)

d3p - A Python Package for Differentially-Private Probabilistic Programming. - Lukas Prediger; Niki Andreas Lopi; Samuel Kaski; Antti Honkela (Proc. Priv. Enhancing Technol., 2022)

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

Individual Privacy Accounting with Gaussian Differential Privacy. - Antti Koskela; Marlon Tobaben; Antti Honkela (ICLR, 2023)

Collaborative Learning From Distributed Data With Differentially Private Synthetic Twin Data. - Lukas Prediger; Joonas Jälkö; Antti Honkela; Samuel Kaski (CoRR, 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)

Differentially private partitioned variational inference. - Mikko A. Heikkilä; Matthew Ashman; Siddharth Swaroop; Richard E. Turner; 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)

Numerical Accounting in the Shuffle Model of Differential Privacy. - Antti Koskela; Mikko A. Heikkilä; Antti Honkela (Trans. Mach. Learn. Res., 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)

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)

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

Understanding Practical Membership Privacy of Deep Learning. - Marlon Tobaben; Gauri Pradhan; Yuan He; 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)

Differentially Private Continual Learning using Pre-Trained Models. - Marlon Tobaben; Marcus Klasson; Rui Li 001; Arno Solin; 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)

A Bias-Variance Decomposition for Ensembles over Multiple Synthetic Datasets. - Ossi Räisä; 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)

(ϵ, δ) Considered Harmful: Best Practices for Reporting Differential Privacy Guarantees. - Juan Felipe Gómez; Bogdan Kulynych; Georgios Kaissis; Jamie Hayes; Borja Balle; Antti Honkela (CoRR, 2025)

Mitigating Disparate Impact of Differentially Private Learning through Bounded Adaptive Clipping. - Linzh Zhao; Aki Rehn; Mikko A. Heikkilä; Razane Tajeddine; Antti Honkela (CoRR, 2025)