Antti Honkela

Publication Activity (10 Years)

Years Active: 2003-2024
Publications (10 Years): 51

Top Topics

Variational Inference

Differential Privacy

Differentially Private

Top Venues

Trans. Mach. Learn. Res.

Publications

Lukas Prediger, Joonas Jälkö, Antti Honkela, Samuel Kaski
Collaborative learning from distributed data with differentially private synthetic data. BMC Medical Informatics Decis. Mak. 24 (1) (2024)
Ossi Räisä, Stratis Markou, Matthew Ashman, Wessel P. Bruinsma, Marlon Tobaben, Antti Honkela, Richard E. Turner
Noise-Aware Differentially Private Regression via Meta-Learning. CoRR (2024)
Marlon Tobaben, Gauri Pradhan, Yuan He, Joonas Jälkö, Antti Honkela
Understanding Practical Membership Privacy of Deep Learning. CoRR (2024)
Ossi Räisä, Joonas Jälkö, Antti Honkela
Subsampling is not Magic: Why Large Batch Sizes Work for Differentially Private Stochastic Optimisation. CoRR (2024)
Sebastian Rodriguez Beltran, Marlon Tobaben, Niki A. Loppi, Antti Honkela
Towards Efficient and Scalable Training of Differentially Private Deep Learning. CoRR (2024)
Ossi Räisä, Antti Honkela
A Bias-Variance Decomposition for Ensembles over Multiple Synthetic Datasets. CoRR (2024)
Antti Koskela, Mikko A. Heikkilä, Antti Honkela
Numerical Accounting in the Shuffle Model of Differential Privacy. Trans. Mach. Learn. Res. 2023 (2023)
Ossi Räisä, Joonas Jälkö, Samuel Kaski, Antti Honkela
Noise-Aware Statistical Inference with Differentially Private Synthetic Data. AISTATS (2023)
Lukas Prediger, Joonas Jälkö, Antti Honkela, Samuel Kaski
Collaborative Learning From Distributed Data With Differentially Private Synthetic Twin Data. CoRR (2023)
Rubèn Tito, Khanh Nguyen, Marlon Tobaben, Raouf Kerkouche, Mohamed Ali Souibgui, Kangsoo Jung, Lei Kang, Ernest Valveny, Antti Honkela, Mario Fritz, Dimosthenis Karatzas
Privacy-Aware Document Visual Question Answering. CoRR (2023)
Marlon Tobaben, Aliaksandra Shysheya, John Bronskill, Andrew Paverd, Shruti Tople, Santiago Zanella Béguelin, Richard E. Turner, Antti Honkela
On the Efficacy of Differentially Private Few-shot Image Classification. Trans. Mach. Learn. Res. 2023 (2023)
Joonas Jälkö, Lukas Prediger, Antti Honkela, Samuel Kaski
DPVIm: Differentially Private Variational Inference Improved. Trans. Mach. Learn. Res. 2023 (2023)
Antti Koskela, Marlon Tobaben, Antti Honkela
Individual Privacy Accounting with Gaussian Differential Privacy. ICLR (2023)
Marlon Tobaben, Aliaksandra Shysheya, John Bronskill, Andrew Paverd, Shruti Tople, Santiago Zanella Béguelin, Richard E. Turner, Antti Honkela
On the Efficacy of Differentially Private Few-shot Image Classification. CoRR (2023)
Mikko A. Heikkilä, Matthew Ashman, Siddharth Swaroop, Richard E. Turner, Antti Honkela
Differentially private partitioned variational inference. Trans. Mach. Learn. Res. 2023 (2023)
Joonas Jälkö, Lukas Prediger, Antti Honkela, Samuel Kaski
DPVIm: Differentially Private Variational Inference Improved. CoRR (2022)
Mikko A. Heikkilä, Matthew Ashman, Siddharth Swaroop, Richard E. Turner, Antti Honkela
Differentially private partitioned variational inference. CoRR (2022)
Antti Koskela, Marlon Tobaben, Antti Honkela
Individual Privacy Accounting with Gaussian Differential Privacy. CoRR (2022)
Lukas Prediger, Niki A. Loppi, Samuel Kaski, Antti Honkela
d3p - A Python Package for Differentially-Private Probabilistic Programming. Proc. Priv. Enhancing Technol. 2022 (2) (2022)
Ossi Räisä, Joonas Jälkö, Samuel Kaski, Antti Honkela
Noise-Aware Statistical Inference with Differentially Private Synthetic Data. CoRR (2022)
Antti Koskela, Antti Honkela
Computing Differential Privacy Guarantees for Heterogeneous Compositions Using FFT. CoRR (2021)
Tejas Kulkarni, Joonas Jälkö, Antti Koskela, Samuel Kaski, Antti Honkela
Differentially Private Bayesian Inference for Generalized Linear Models. ICML (2021)
Antti Honkela
Gaussian Processes with Differential Privacy. CoRR (2021)
Joonas Jälkö, Eemil Lagerspetz, Jari Haukka, Sasu Tarkoma, Antti Honkela, Samuel Kaski
Privacy-preserving data sharing via probabilistic modeling. Patterns 2 (7) (2021)
Antti Koskela, Joonas Jälkö, Lukas Prediger, Antti Honkela
Tight Differential Privacy for Discrete-Valued Mechanisms and for the Subsampled Gaussian Mechanism Using FFT. AISTATS (2021)
Antti Koskela, Mikko A. Heikkilä, Antti Honkela
Tight Accounting in the Shuffle Model of Differential Privacy. CoRR (2021)
Ossi Räisä, Antti Koskela, Antti Honkela
Differentially Private Hamiltonian Monte Carlo. CoRR (2021)
Tejas Kulkarni, Joonas Jälkö, Samuel Kaski, Antti Honkela
Locally Differentially Private Bayesian Inference. CoRR (2021)
Lukas Prediger, Niki Loppi, Samuel Kaski, Antti Honkela
d3p - A Python Package for Differentially-Private Probabilistic Programming. CoRR (2021)
Antti Koskela, Antti Honkela
Learning Rate Adaptation for Differentially Private Learning. AISTATS (2020)
Razane Tajeddine, Joonas Jälkö, Samuel Kaski, Antti Honkela
Privacy-preserving Data Sharing on Vertically Partitioned Data. CoRR (2020)
Antti Koskela, Joonas Jälkö, Antti Honkela
Computing Tight Differential Privacy Guarantees Using FFT. AISTATS (2020)
Antti Koskela, Joonas Jälkö, Lukas Prediger, Antti Honkela
Tight Approximate Differential Privacy for Discrete-Valued Mechanisms Using FFT. CoRR (2020)
Mikko A. Heikkilä, Antti Koskela, Kana Shimizu, Samuel Kaski, Antti Honkela
Differentially private cross-silo federated learning. CoRR (2020)
Tejas Kulkarni, Joonas Jälkö, Antti Koskela, Samuel Kaski, Antti Honkela
Differentially Private Bayesian Inference for Generalized Linear Models. CoRR (2020)
Joonas Jälkö, Eemil Lagerspetz, Jari Haukka, Sasu Tarkoma, Samuel Kaski, Antti Honkela
Privacy-preserving data sharing via probabilistic modelling. CoRR (2019)
Antti Koskela, Joonas Jälkö, Antti Honkela
Computing Exact Guarantees for Differential Privacy. CoRR (2019)
Mikko A. Heikkilä, Joonas Jälkö, Onur Dikmen, Antti Honkela
Differentially Private Markov Chain Monte Carlo. CoRR (2019)
Teppo Mikael Niinimäki, Mikko A. Heikkilä, Antti Honkela, Samuel Kaski
Representation transfer for differentially private drug sensitivity prediction. Bioinform. 35 (14) (2019)
Mrinank Sharma, Michael Hutchinson, Siddharth Swaroop, Antti Honkela, Richard E. Turner
Differentially Private Federated Variational Inference. CoRR (2019)
Mikko A. Heikkilä, Joonas Jälkö, Onur Dikmen, Antti Honkela
Differentially Private Markov Chain Monte Carlo. NeurIPS (2019)
Teppo Mikael Niinimäki, Mikko A. Heikkilä, Antti Honkela, Samuel Kaski
Representation Transfer for Differentially Private Drug Sensitivity Prediction. CoRR (2019)
Antti Koskela, Antti Honkela
Learning rate adaptation for differentially private stochastic gradient descent. CoRR (2018)
Mikko A. Heikkilä, Eemil Lagerspetz, Samuel Kaski, Kana Shimizu, Sasu Tarkoma, Antti Honkela
Differentially private Bayesian learning on distributed data. NIPS (2017)
Mikko A. Heikkilä, Yusuke Okimoto, Samuel Kaski, Kana Shimizu, Antti Honkela
Differentially Private Bayesian Learning on Distributed Data. CoRR (2017)
Joonas Jälkö, Antti Honkela, Onur Dikmen
Differentially Private Variational Inference for Non-conjugate Models. UAI (2017)
Joonas Jälkö, Onur Dikmen, Antti Honkela
Differentially Private Variational Inference for Non-conjugate Models. CoRR (2016)
Otte Heinävaara, Janne Leppä-aho, Jukka Corander, Antti Honkela
On the inconsistency of ℓ 1-penalised sparse precision matrix estimation. BMC Bioinform. 17 (S-16) (2016)
Hande Topa, Antti Honkela
Analysis of differential splicing suggests different modes of short-term splicing regulation. Bioinform. 32 (12) (2016)
Antti Honkela, Mrinal Das, Onur Dikmen, Samuel Kaski
Efficient differentially private learning improves drug sensitivity prediction. CoRR (2016)
Otte Heinävaara, Janne Leppä-aho, Jukka Corander, Antti Honkela
-penalised sparse precision matrix estimation. CoRR (2016)
James Hensman, Panagiotis Papastamoulis, Peter Glaus, Antti Honkela, Magnus Rattray
Fast and accurate approximate inference of transcript expression from RNA-seq data. Bioinform. 31 (24) (2015)
Hande Topa, Ágnes Jónás, Robert Kofler, Carolin Kosiol, Antti Honkela
Gaussian process test for high-throughput sequencing time series: application to experimental evolution. Bioinform. 31 (11) (2015)
Hande Topa, Antti Honkela
Gaussian process modelling of multiple short time series. ESANN (2015)
Sohan Seth, Niko Välimäki, Samuel Kaski, Antti Honkela
Exploration and retrieval of whole-metagenome sequencing samples. Bioinform. 30 (17) (2014)
Ciira Wa Maina, Antti Honkela, Filomena Matarese, Korbinian Grote, Hendrik G. Stunnenberg, George Reid, Neil D. Lawrence, Magnus Rattray
Inference of RNA Polymerase II Transcription Dynamics from Chromatin Immunoprecipitation Time Course Data. PLoS Comput. Biol. 10 (5) (2014)
Sohan Seth, Niko Välimäki, Samuel Kaski, Antti Honkela
Exploration and retrieval of whole-metagenome sequencing samples. CoRR (2013)
Peter Glaus, Antti Honkela, Magnus Rattray
Identifying differentially expressed transcripts from RNA-seq data with biological variation. Bioinform. 28 (13) (2012)
Michalis K. Titsias, Antti Honkela, Neil D. Lawrence, Magnus Rattray
Identifying targets of multiple co-regulating transcription factors from expression time-series by Bayesian model comparison. BMC Syst. Biol. 6 (2012)
Markus Harva, Tapani Raiko, Antti Honkela, Harri Valpola, Juha Karhunen
Bayes Blocks: An Implementation of the Variational Bayesian Building Blocks Framework CoRR (2012)
Antti Honkela, Pei Gao, Jonatan Ropponen, Magnus Rattray, Neil D. Lawrence
tigre: Transcription factor inference through gaussian process reconstruction of expression for bioconductor. Bioinform. 27 (7) (2011)
Ulpu Remes, Kalle J. Palomäki, Tapani Raiko, Antti Honkela, Mikko Kurimo
Missing-Feature Reconstruction With a Bounded Nonlinear State-Space Model. IEEE Signal Process. Lett. 18 (10) (2011)
Bjoern H. Menze, Koen Van Leemput, Antti Honkela, Ender Konukoglu, Marc-André Weber, Nicholas Ayache, Polina Golland
A Generative Approach for Image-Based Modeling of Tumor Growth. IPMI (2011)
Antti Honkela, Charles Girardot, Eleanor Hilary Gustafson, Ya-Hsin Liu, Eileen E. M. Furlong, Neil D. Lawrence, Magnus Rattray
Model-based method for transcription factor target identification with limited data. Proc. Natl. Acad. Sci. USA 107 (17) (2010)
Antti Honkela, Tapani Raiko, Mikael Kuusela, Matti Tornio, Juha Karhunen
Approximate Riemannian Conjugate Gradient Learning for Fixed-Form Variational Bayes. J. Mach. Learn. Res. 11 (2010)
Mikael Kuusela, Tapani Raiko, Antti Honkela, Juha Karhunen
A gradient-based algorithm competitive with variational Bayesian EM for mixture of Gaussians. IJCNN (2009)
Antti Honkela, Jeremias Seppä, Esa Alhoniemi
Agglomerative independent variable group analysis. Neurocomputing 71 (7-9) (2008)
Pei Gao, Antti Honkela, Magnus Rattray, Neil D. Lawrence
Gaussian process modelling of latent chemical species: applications to inferring transcription factor activities. ECCB (2008)
Antti Honkela, Matti Tornio, Tapani Raiko, Juha Karhunen
Natural Conjugate Gradient in Variational Inference. ICONIP (2) (2007)
Antti Honkela, Jeremias Seppä, Esa Alhoniemi
Agglomerative Independent Variable Group Analysis. ESANN (2007)
Antti Honkela, Harri Valpola, Alexander Ilin, Juha Karhunen
Blind separation of nonlinear mixtures by variational Bayesian learning. Digit. Signal Process. 17 (5) (2007)
Esa Alhoniemi, Antti Honkela, Krista Lagus, Santeri Jeremias Seppä, Paul Wagner, Harri Valpola
Compact Modeling of Data Using Independent Variable Group Analysis. IEEE Trans. Neural Networks 18 (6) (2007)
Tapani Raiko, Matti Tornio, Antti Honkela, Juha Karhunen
State Inference in Variational Bayesian Nonlinear State-Space Models. ICA (2006)
Antti Honkela, Tomas Östman, Ricardo Vigário
Empirical evidence of the linear nature of magnetoencephalograms. ESANN (2005)
Markus Harva, Tapani Raiko, Antti Honkela, Harri Valpola, Juha Karhunen
Bayes Blocks: An Implementation of the Variational Bayesian Building Blocks Framework. UAI (2005)
Antti Honkela, Harri Valpola
Unsupervised Variational Bayesian Learning of Nonlinear Models. NIPS (2004)
Antti Honkela, Harri Valpola
Variational learning and bits-back coding: an information-theoretic view to Bayesian learning. IEEE Trans. Neural Networks 15 (4) (2004)
Alexander Ilin, Antti Honkela
Post-nonlinear Independent Component Analysis by Variational Bayesian Learning. ICA (2004)
Antti Honkela, Stefan Harmeling, Leo Lundqvist, Harri Valpola
Using Kernel PCA for Initialisation of Variational Bayesian Nonlinear Blind Source Separation Method. ICA (2004)
Antti Honkela, Harri Valpola, Juha Karhunen
Accelerating Cyclic Update Algorithms for Parameter Estimation by Pattern Searches. Neural Process. Lett. 17 (2) (2003)
Harri Valpola, Erkki Oja, Alexander Ilin, Antti Honkela, Juha Karhunen
Nonlinear Blind Source Separation by Variational Bayesian Learning. IEICE Trans. Fundam. Electron. Commun. Comput. Sci. (3) (2003)