default search action
Google
Google Scholar
Semantic Scholar
Internet Archive Scholar
CiteSeerX
ORCIDAntti Honkela
Person information
- affiliation: University of Helsinki, Finland
- affiliation: Aalto University, Finland
Refine list
refinements active!
zoomed in on ?? of ?? records
view refined list in
export refined list as
showing all ?? records
2020 – today
- 2024
[j26]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): 167 (2024)- [c23]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:
Privacy-Aware Document Visual Question Answering. ICDAR (6) 2024: 199-218 - [c22]Ossi Räisä, Joonas Jälkö, Antti Honkela:
Subsampling is not Magic: Why Large Batch Sizes Work for Differentially Private Stochastic Optimisation. ICML 2024 - [i34]Ossi Räisä, Antti Honkela:
A Bias-Variance Decomposition for Ensembles over Multiple Synthetic Datasets. CoRR abs/2402.03985 (2024) - [i33]Ossi Räisä, Joonas Jälkö, Antti Honkela:
Subsampling is not Magic: Why Large Batch Sizes Work for Differentially Private Stochastic Optimisation. CoRR abs/2402.03990 (2024) - [i32]Marlon Tobaben, Gauri Pradhan
, Yuan He, Joonas Jälkö, Antti Honkela:
Understanding Practical Membership Privacy of Deep Learning. CoRR abs/2402.06674 (2024) - [i31]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 abs/2406.08569 (2024) - [i30]Sebastian Rodriguez Beltran, Marlon Tobaben, Niki A. Loppi, Antti Honkela:
Towards Efficient and Scalable Training of Differentially Private Deep Learning. CoRR abs/2406.17298 (2024) - 2023
- [j25]Mikko A. Heikkilä, Matthew Ashman, Siddharth Swaroop, Richard E. Turner, Antti Honkela:
Differentially private partitioned variational inference. Trans. Mach. Learn. Res. 2023 (2023) - [j24]Joonas Jälkö, Lukas Prediger, Antti Honkela, Samuel Kaski:
DPVIm: Differentially Private Variational Inference Improved. Trans. Mach. Learn. Res. 2023 (2023) - [j23]Antti Koskela, Mikko A. Heikkilä, Antti Honkela:
Numerical Accounting in the Shuffle Model of Differential Privacy. Trans. Mach. Learn. Res. 2023 (2023) - [j22]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) - [c21]Ossi Räisä, Joonas Jälkö, Samuel Kaski, Antti Honkela:
Noise-Aware Statistical Inference with Differentially Private Synthetic Data. AISTATS 2023: 3620-3643 - [c20]Antti Koskela, Marlon Tobaben, Antti Honkela:
Individual Privacy Accounting with Gaussian Differential Privacy. ICLR 2023 - [i29]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 abs/2302.01190 (2023) - [i28]Lukas Prediger, Joonas Jälkö, Antti Honkela, Samuel Kaski:
Collaborative Learning From Distributed Data With Differentially Private Synthetic Twin Data. CoRR abs/2308.04755 (2023) - [i27]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 abs/2312.10108 (2023) - 2022
- [j21]Lukas Prediger, Niki A. Loppi, Samuel Kaski, Antti Honkela:
d3p - A Python Package for Differentially-Private Probabilistic Programming. Proc. Priv. Enhancing Technol. 2022(2): 407-425 (2022) - [i26]Ossi Räisä, Joonas Jälkö, Samuel Kaski, Antti Honkela:
Noise-Aware Statistical Inference with Differentially Private Synthetic Data. CoRR abs/2205.14485 (2022) - [i25]Mikko A. Heikkilä, Matthew Ashman, Siddharth Swaroop, Richard E. Turner, Antti Honkela:
Differentially private partitioned variational inference. CoRR abs/2209.11595 (2022) - [i24]Antti Koskela, Marlon Tobaben, Antti Honkela:
Individual Privacy Accounting with Gaussian Differential Privacy. CoRR abs/2209.15596 (2022) - [i23]Joonas Jälkö, Lukas Prediger, Antti Honkela, Samuel Kaski:
DPVIm: Differentially Private Variational Inference Improved. CoRR abs/2210.15961 (2022) - 2021
- [j20]Joonas Jälkö, Eemil Lagerspetz, Jari Haukka, Sasu Tarkoma, Antti Honkela, Samuel Kaski:
Privacy-preserving data sharing via probabilistic modeling. Patterns 2(7): 100271 (2021) - [c19]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: 3358-3366 - [c18]Tejas Kulkarni, Joonas Jälkö, Antti Koskela, Samuel Kaski, Antti Honkela:
Differentially Private Bayesian Inference for Generalized Linear Models. ICML 2021: 5838-5849 - [i22]Antti Koskela, Antti Honkela:
Computing Differential Privacy Guarantees for Heterogeneous Compositions Using FFT. CoRR abs/2102.12412 (2021) - [i21]Lukas Prediger, Niki A. Loppi, Samuel Kaski, Antti Honkela:
d3p - A Python Package for Differentially-Private Probabilistic Programming. CoRR abs/2103.11648 (2021) - [i20]Antti Honkela:
Gaussian Processes with Differential Privacy. CoRR abs/2106.00474 (2021) - [i19]Antti Koskela, Mikko A. Heikkilä, Antti Honkela:
Tight Accounting in the Shuffle Model of Differential Privacy. CoRR abs/2106.00477 (2021) - [i18]Ossi Räisä, Antti Koskela, Antti Honkela:
Differentially Private Hamiltonian Monte Carlo. CoRR abs/2106.09376 (2021) - [i17]Tejas Kulkarni, Joonas Jälkö, Samuel Kaski, Antti Honkela:
Locally Differentially Private Bayesian Inference. CoRR abs/2110.14426 (2021) - 2020
- [c17]Antti Koskela, Antti Honkela:
Learning Rate Adaptation for Differentially Private Learning. AISTATS 2020: 2465-2475 - [c16]Antti Koskela, Joonas Jälkö, Antti Honkela:
Computing Tight Differential Privacy Guarantees Using FFT. AISTATS 2020: 2560-2569 - [i16]Antti Koskela, Joonas Jälkö, Lukas Prediger, Antti Honkela:
Tight Approximate Differential Privacy for Discrete-Valued Mechanisms Using FFT. CoRR abs/2006.07134 (2020) - [i15]Mikko A. Heikkilä, Antti Koskela, Kana Shimizu, Samuel Kaski, Antti Honkela:
Differentially private cross-silo federated learning. CoRR abs/2007.05553 (2020) - [i14]Razane Tajeddine, Joonas Jälkö, Samuel Kaski, Antti Honkela:
Privacy-preserving Data Sharing on Vertically Partitioned Data. CoRR abs/2010.09293 (2020) - [i13]Tejas Kulkarni, Joonas Jälkö, Antti Koskela, Samuel Kaski, Antti Honkela:
Differentially Private Bayesian Inference for Generalized Linear Models. CoRR abs/2011.00467 (2020)
2010 – 2019
- 2019
- [j19]Teppo Mikael Niinimäki, Mikko A. Heikkilä, Antti Honkela, Samuel Kaski:
Representation transfer for differentially private drug sensitivity prediction. Bioinform. 35(14): i218-i224 (2019) - [c15]Mikko A. Heikkilä, Joonas Jälkö, Onur Dikmen, Antti Honkela:
Differentially Private Markov Chain Monte Carlo. NeurIPS 2019: 4115-4125 - [i12]Teppo Mikael Niinimäki, Mikko A. Heikkilä, Antti Honkela, Samuel Kaski:
Representation Transfer for Differentially Private Drug Sensitivity Prediction. CoRR abs/1901.10227 (2019) - [i11]Mikko A. Heikkilä, Joonas Jälkö, Onur Dikmen, Antti Honkela:
Differentially Private Markov Chain Monte Carlo. CoRR abs/1901.10275 (2019) - [i10]Antti Koskela, Joonas Jälkö, Antti Honkela:
Computing Exact Guarantees for Differential Privacy. CoRR abs/1906.03049 (2019) - [i9]Mrinank Sharma, Michael J. Hutchinson, Siddharth Swaroop, Antti Honkela, Richard E. Turner:
Differentially Private Federated Variational Inference. CoRR abs/1911.10563 (2019) - [i8]Joonas Jälkö, Eemil Lagerspetz, Jari Haukka, Sasu Tarkoma, Samuel Kaski, Antti Honkela:
Privacy-preserving data sharing via probabilistic modelling. CoRR abs/1912.04439 (2019) - 2018
- [i7]Antti Koskela, Antti Honkela:
Learning rate adaptation for differentially private stochastic gradient descent. CoRR abs/1809.03832 (2018) - 2017
- [c14]Mikko A. Heikkilä, Eemil Lagerspetz, Samuel Kaski, Kana Shimizu, Sasu Tarkoma, Antti Honkela:
Differentially private Bayesian learning on distributed data. NIPS 2017: 3226-3235 - [c13]Joonas Jälkö, Antti Honkela, Onur Dikmen:
Differentially Private Variational Inference for Non-conjugate Models. UAI 2017 - [i6]Mikko A. Heikkilä, Yusuke Okimoto, Samuel Kaski, Kana Shimizu, Antti Honkela:
Differentially Private Bayesian Learning on Distributed Data. CoRR abs/1703.01106 (2017) - 2016
- [j18]Hande Topa, Antti Honkela:
Analysis of differential splicing suggests different modes of short-term splicing regulation. Bioinform. 32(12): 147-155 (2016) - [j17]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): 99-107 (2016) - [i5]Otte Heinävaara, Janne Leppä-aho, Jukka Corander, Antti Honkela:
On the inconsistency of ℓ1-penalised sparse precision matrix estimation. CoRR abs/1603.02532 (2016) - [i4]Antti Honkela, Mrinal Das, Onur Dikmen, Samuel Kaski:
Efficient differentially private learning improves drug sensitivity prediction. CoRR abs/1606.02109 (2016) - [i3]Joonas Jälkö, Onur Dikmen, Antti Honkela:
Differentially Private Variational Inference for Non-conjugate Models. CoRR abs/1610.08749 (2016) - 2015
- [j16]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): 1762-1770 (2015) - [j15]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): 3881-3889 (2015) - [c12]Hande Topa, Antti Honkela:
Gaussian process modelling of multiple short time series. ESANN 2015 - 2014
- [j14]Sohan Seth, Niko Välimäki, Samuel Kaski, Antti Honkela:
Exploration and retrieval of whole-metagenome sequencing samples. Bioinform. 30(17): 2471-2479 (2014) - [j13]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) - 2013
- [i2]Sohan Seth, Niko Välimäki, Samuel Kaski, Antti Honkela:
Exploration and retrieval of whole-metagenome sequencing samples. CoRR abs/1308.6074 (2013) - 2012
- [j12]Peter Glaus, Antti Honkela, Magnus Rattray:
Identifying differentially expressed transcripts from RNA-seq data with biological variation. Bioinform. 28(13): 1721-1728 (2012) - [j11]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: 53 (2012) - [i1]Markus Harva, Tapani Raiko, Antti Honkela, Harri Valpola, Juha Karhunen:
Bayes Blocks: An Implementation of the Variational Bayesian Building Blocks Framework. CoRR abs/1207.1380 (2012) - 2011
- [j10]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): 1026-1027 (2011) - [j9]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): 563-566 (2011) - [c11]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: 735-747 - 2010
- [j8]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: 3235-3268 (2010) - [j7]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): 7793-7798 (2010)
2000 – 2009
- 2009
- [c10]Mikael Kuusela, Tapani Raiko, Antti Honkela, Juha Karhunen:
A gradient-based algorithm competitive with variational Bayesian EM for mixture of Gaussians. IJCNN 2009: 1688-1695 - 2008
- [j6]Antti Honkela, Jeremias Seppä, Esa Alhoniemi:
Agglomerative independent variable group analysis. Neurocomputing 71(7-9): 1311-1320 (2008) - [c9]Pei Gao, Antti Honkela, Magnus Rattray, Neil D. Lawrence:
Gaussian process modelling of latent chemical species: applications to inferring transcription factor activities. ECCB 2008: 70-75 - 2007
- [j5]Antti Honkela, Harri Valpola, Alexander Ilin, Juha Karhunen:
Blind separation of nonlinear mixtures by variational Bayesian learning. Digit. Signal Process. 17(5): 914-934 (2007) - [j4]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): 1762-1776 (2007) - [c8]Antti Honkela, Jeremias Seppä, Esa Alhoniemi:
Agglomerative Independent Variable Group Analysis. ESANN 2007: 55-60 - [c7]Antti Honkela, Matti Tornio, Tapani Raiko, Juha Karhunen:
Natural Conjugate Gradient in Variational Inference. ICONIP (2) 2007: 305-314 - 2006
- [c6]Tapani Raiko, Matti Tornio, Antti Honkela, Juha Karhunen:
State Inference in Variational Bayesian Nonlinear State-Space Models. ICA 2006: 222-229 - 2005
- [c5]Antti Honkela, Tomas Östman, Ricardo Vigário:
Empirical evidence of the linear nature of magnetoencephalograms. ESANN 2005: 285-290 - [c4]Markus Harva, Tapani Raiko, Antti Honkela, Harri Valpola, Juha Karhunen:
Bayes Blocks: An Implementation of the Variational Bayesian Building Blocks Framework. UAI 2005: 259-266 - 2004
- [j3]Antti Honkela, Harri Valpola:
Variational learning and bits-back coding: an information-theoretic view to Bayesian learning. IEEE Trans. Neural Networks 15(4): 800-810 (2004) - [c3]Alexander Ilin, Antti Honkela:
Post-nonlinear Independent Component Analysis by Variational Bayesian Learning. ICA 2004: 766-773 - [c2]Antti Honkela, Stefan Harmeling, Leo Lundqvist, Harri Valpola:
Using Kernel PCA for Initialisation of Variational Bayesian Nonlinear Blind Source Separation Method. ICA 2004: 790-797 - [c1]Antti Honkela, Harri Valpola:
Unsupervised Variational Bayesian Learning of Nonlinear Models. NIPS 2004: 593-600 - 2003
- [j2]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. 86-A(3): 532-541 (2003) - [j1]Antti Honkela, Harri Valpola, Juha Karhunen:
Accelerating Cyclic Update Algorithms for Parameter Estimation by Pattern Searches. Neural Process. Lett. 17(2): 191-203 (2003)
Coauthor Index
manage site settings
To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.
Unpaywalled article links
Add open access links from 
to the list of external document links (if available).
Privacy notice: By enabling the option above, your browser will contact the API of unpaywall.org to load hyperlinks to open access articles. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Unpaywall privacy policy.
Archived links via Wayback Machine
For web page which are no longer available, try to retrieve content from the 
of the Internet Archive (if available).
Privacy notice: By enabling the option above, your browser will contact the API of archive.org to check for archived content of web pages that are no longer available. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Internet Archive privacy policy.
Reference lists
Add a list of references from 
, 
, and 
to record detail pages.
load references from crossref.org and opencitations.net
Privacy notice: By enabling the option above, your browser will contact the APIs of crossref.org, opencitations.net, and semanticscholar.org to load article reference information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Crossref privacy policy and the OpenCitations privacy policy, as well as the AI2 Privacy Policy covering Semantic Scholar.
Citation data
Add a list of citing articles from and to record detail pages.
load citations from opencitations.net
Privacy notice: By enabling the option above, your browser will contact the API of opencitations.net and semanticscholar.org to load citation information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the OpenCitations privacy policy as well as the AI2 Privacy Policy covering Semantic Scholar.
OpenAlex data
Load additional information about publications from 
.
Privacy notice: By enabling the option above, your browser will contact the API of openalex.org to load additional information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the information given by OpenAlex.
last updated on 2024-10-08 21:27 CEST by the dblp team
all metadata released as open data under CC0 1.0 license
see also: Terms of Use | Privacy Policy | Imprint
dblp was originally created in 1993 at:
since 2018, dblp has been operated and maintained by:
the dblp computer science bibliography is funded and supported by:
