Markus Heinonen

Publication Activity (10 Years)

Years Active: 2006-2024
Publications (10 Years): 80

Top Topics

Bayesian Approaches

Reinforcement Learning

Diffusion Models

Gaussian Process

Top Venues

Publications

Trung Q. Trinh, Markus Heinonen, Luigi Acerbi, Samuel Kaski
Input-gradient space particle inference for neural network ensembles. ICLR (2024)
Markus Heinonen, Ba-Hien Tran, Michael Kampffmeyer, Maurizio Filippone
Robust Classification by Coupling Data Mollification with Label Smoothing. CoRR (2024)
Severi Rissanen, Markus Heinonen, Arno Solin
Improving Discrete Diffusion Models via Structured Preferential Generation. CoRR (2024)
Yogesh Verma, Markus Heinonen, Vikas Garg
ClimODE: Climate and Weather Forecasting with Physics-informed Neural ODEs. CoRR (2024)
Trung Trinh, Markus Heinonen, Luigi Acerbi, Samuel Kaski
Improving robustness to corruptions with multiplicative weight perturbations. CoRR (2024)
Najwa Laabid, Severi Rissanen, Markus Heinonen, Arno Solin, Vikas Garg
Alignment is Key for Applying Diffusion Models to Retrosynthesis. CoRR (2024)
Alexandru Dumitrescu, Dani Korpela, Markus Heinonen, Yogesh Verma, Valerii Iakovlev, Vikas Garg, Harri Lähdesmäki
Field-based Molecule Generation. CoRR (2024)
Yogesh Verma, Markus Heinonen, Vikas Garg
ClimODE: Climate and Weather Forecasting with Physics-informed Neural ODEs. ICLR (2024)
Aarne Talman, Hande Çelikkanat, Sami Virpioja, Markus Heinonen, Jörg Tiedemann
Uncertainty-Aware Natural Language Inference with Stochastic Weight Averaging. CoRR (2023)
Valerii Iakovlev, Markus Heinonen, Harri Lähdesmäki
Learning Space-Time Continuous Neural PDEs from Partially Observed States. CoRR (2023)
Giulio Franzese, Simone Rossi, Dario Rossi, Markus Heinonen, Maurizio Filippone, Pietro Michiardi
Continuous-Time Functional Diffusion Processes. CoRR (2023)
Oliver Struckmeier, Ievgen Redko, Anton Mallasto, Karol Arndt, Markus Heinonen, Ville Kyrki
Beyond invariant representation learning: linearly alignable latent spaces for efficient closed-form domain adaptation. CoRR (2023)
Giulio Franzese, Giulio Corallo, Simone Rossi, Markus Heinonen, Maurizio Filippone, Pietro Michiardi
Continuous-Time Functional Diffusion Processes. NeurIPS (2023)
Valerii Iakovlev, Çagatay Yildiz, Markus Heinonen, Harri Lähdesmäki
Latent Neural ODEs with Sparse Bayesian Multiple Shooting. ICLR (2023)
Magnus Ross, Markus Heinonen
Learning Energy Conserving Dynamics Efficiently with Hamiltonian Gaussian Processes. Trans. Mach. Learn. Res. 2023 (2023)
Yogesh Verma, Markus Heinonen, Vikas Garg
AbODE: Ab Initio Antibody Design using Conjoined ODEs. CoRR (2023)
Quentin Bouniot, Ievgen Redko, Anton Mallasto, Charlotte Laclau, Karol Arndt, Oliver Struckmeier, Markus Heinonen, Ville Kyrki, Samuel Kaski
Understanding deep neural networks through the lens of their non-linearity. CoRR (2023)
Emmi Jokinen, Alexandru Dumitrescu, Jani Huuhtanen, Vladimir Gligorijevic, Satu Mustjoki, Richard Bonneau, Markus Heinonen, Harri Lähdesmäki
TCRconv: predicting recognition between T cell receptors and epitopes using contextualized motifs. Bioinform. 39 (1) (2023)
Trung Q. Trinh, Markus Heinonen, Luigi Acerbi, Samuel Kaski
Input gradient diversity for neural network ensembles. CoRR (2023)
Vishnu Raj, Tianyu Cui, Markus Heinonen, Pekka Marttinen
Incorporating functional summary information in Bayesian neural networks using a Dirichlet process likelihood approach. AISTATS (2023)
Magnus Ross, Markus Heinonen
Learning Energy Conserving Dynamics Efficiently with Hamiltonian Gaussian Processes. CoRR (2023)
Oliver Struckmeier, Ievgen Redko, Anton Mallasto, Karol Arndt, Markus Heinonen, Ville Kyrki
Learning representations that are closed-form Monge mapping optimal with application to domain adaptation. Trans. Mach. Learn. Res. 2023 (2023)
Yogesh Verma, Markus Heinonen, Vikas Garg
AbODE: Ab initio antibody design using conjoined ODEs. ICML (2023)
Valerii Iakovlev, Markus Heinonen, Harri Lähdesmäki
Learning Space-Time Continuous Latent Neural PDEs from Partially Observed States. NeurIPS (2023)
Severi Rissanen, Markus Heinonen, Arno Solin
Generative Modelling with Inverse Heat Dissipation. ICLR (2023)
Aarne Talman, Hande Çelikkanat, Sami Virpioja, Markus Heinonen, Jörg Tiedemann
Uncertainty-Aware Natural Language Inference with Stochastic Weight Averaging. NoDaLiDa (2023)
Valerii Iakovlev, Çagatay Yildiz, Markus Heinonen, Harri Lähdesmäki
Latent Neural ODEs with Sparse Bayesian Multiple Shooting. CoRR (2022)
Yogesh Verma, Samuel Kaski, Markus Heinonen, Vikas Garg
Modular Flows: Differential Molecular Generation. CoRR (2022)
Yogesh Verma, Samuel Kaski, Markus Heinonen, Vikas Garg
Modular Flows: Differential Molecular Generation. NeurIPS (2022)
Trung Q. Trinh, Markus Heinonen, Luigi Acerbi, Samuel Kaski
Tackling covariate shift with node-based Bayesian neural networks. CoRR (2022)
Pashupati Hegde, Çagatay Yildiz, Harri Lähdesmäki, Samuel Kaski, Markus Heinonen
Variational multiple shooting for Bayesian ODEs with Gaussian processes. UAI (2022)
Iiris Sundin, Alexey Voronov, Haoping Xiao, Kostas Papadopoulos, Esben Jannik Bjerrum, Markus Heinonen, Atanas Patronov, Samuel Kaski, Ola Engkvist
Human-in-the-loop assisted de novo molecular design. J. Cheminformatics 14 (1) (2022)
Severi Rissanen, Markus Heinonen, Arno Solin
Generative Modelling With Inverse Heat Dissipation. CoRR (2022)
Alexander Aushev, Henri Pesonen, Markus Heinonen, Jukka Corander, Samuel Kaski
Likelihood-free inference with deep Gaussian processes. Comput. Stat. Data Anal. 174 (2022)
Trung Q. Trinh, Markus Heinonen, Luigi Acerbi, Samuel Kaski
Tackling covariate shift with node-based Bayesian neural networks. ICML (2022)
David Duvenaud, Markus Heinonen, Michael Tiemann, Max Welling
Differential Equations and Continuous-Time Deep Learning (Dagstuhl Seminar 22332). Dagstuhl Reports 12 (8) (2022)
Anni A. Antikainen, Markus Heinonen, Harri Lähdesmäki
Modeling binding specificities of transcription factor pairs with random forests. BMC Bioinform. 23 (1) (2022)
Vishnu Raj, Tianyu Cui, Markus Heinonen, Pekka Marttinen
Look beyond labels: Incorporating functional summary information in Bayesian neural networks. CoRR (2022)
Zheyang Shen, Markus Heinonen, Samuel Kaski
De-randomizing MCMC dynamics with the diffusion Stein operator. CoRR (2021)
Çagatay Yildiz, Markus Heinonen, Harri Lähdesmäki
Continuous-time Model-based Reinforcement Learning. ICML (2021)
Çagatay Yildiz, Markus Heinonen, Harri Lähdesmäki
Continuous-Time Model-Based Reinforcement Learning. CoRR (2021)
Emmi Jokinen, Jani Huuhtanen, Satu Mustjoki, Markus Heinonen, Harri Lähdesmäki
Predicting recognition between T cell receptors and epitopes with TCRGP. PLoS Comput. Biol. 17 (3) (2021)
Zheyang Shen, Markus Heinonen, Samuel Kaski
De-randomizing MCMC dynamics with the diffusion Stein operator. NeurIPS (2021)
Anton Mallasto, Markus Heinonen, Samuel Kaski
Bayesian Inference for Optimal Transport with Stochastic Cost. ACML (2021)
Pashupati Hegde, Çagatay Yildiz, Harri Lähdesmäki, Samuel Kaski, Markus Heinonen
Bayesian inference of ODEs with Gaussian processes. CoRR (2021)
Valerii Iakovlev, Markus Heinonen, Harri Lähdesmäki
Learning continuous-time PDEs from sparse data with graph neural networks. ICLR (2021)
Anton Mallasto, Karol Arndt, Markus Heinonen, Samuel Kaski, Ville Kyrki
Affine Transport for Sim-to-Real Domain Adaptation. CoRR (2021)
Simone Rossi, Markus Heinonen, Edwin V. Bonilla, Zheyang Shen, Maurizio Filippone
Sparse Gaussian Processes Revisited: Bayesian Approaches to Inducing-Variable Approximations. AISTATS (2021)
David Blanco Mulero, Markus Heinonen, Ville Kyrki
Evolving-Graph Gaussian Processes. CoRR (2021)
Charles W. L. Gadd, Markus Heinonen, Harri Lähdesmäki, Samuel Kaski
Sample-efficient reinforcement learning using deep Gaussian processes. CoRR (2020)
Trung Trinh, Samuel Kaski, Markus Heinonen
Scalable Bayesian neural networks by layer-wise input augmentation. CoRR (2020)
Simone Rossi, Markus Heinonen, Edwin V. Bonilla, Zheyang Shen, Maurizio Filippone
Rethinking Sparse Gaussian Processes: Bayesian Approaches to Inducing-Variable Approximations. CoRR (2020)
Anton Mallasto, Markus Heinonen, Samuel Kaski
Bayesian Inference for Optimal Transport with Stochastic Cost. CoRR (2020)
Valerii Iakovlev, Markus Heinonen, Harri Lähdesmäki
Learning continuous-time PDEs from sparse data with graph neural networks. CoRR (2020)
Zheyang Shen, Markus Heinonen, Samuel Kaski
Learning spectrograms with convolutional spectral kernels. AISTATS (2020)
Alexander Aushev, Henri Pesonen, Markus Heinonen, Jukka Corander, Samuel Kaski
Likelihood-Free Inference with Deep Gaussian Processes. CoRR (2020)
Markus Heinonen, Maria Osmala, Henrik Mannerström, Janne Wallenius, Samuel Kaski, Juho Rousu, Harri Lähdesmäki
Bayesian metabolic flux analysis reveals intracellular flux couplings. Bioinform. 35 (14) (2019)
Çagatay Yildiz, Markus Heinonen, Harri Lähdesmäki
ODE2VAE: Deep generative second order ODEs with Bayesian neural networks. NeurIPS (2019)
Zheyang Shen, Markus Heinonen, Samuel Kaski
Harmonizable mixture kernels with variational Fourier features. AISTATS (2019)
Çagatay Yildiz, Markus Heinonen, Harri Lähdesmäki
ODE$^2$VAE: Deep generative second order ODEs with Bayesian neural networks. CoRR (2019)
Kenneth Blomqvist, Samuel Kaski, Markus Heinonen
Deep Convolutional Gaussian Processes. ECML/PKDD (2) (2019)
Pashupati Hegde, Markus Heinonen, Harri Lähdesmäki, Samuel Kaski
Deep learning with differential Gaussian process flows. AISTATS (2019)
Zheyang Shen, Markus Heinonen, Samuel Kaski
Learning spectrograms with convolutional spectral kernels. CoRR (2019)
Sami Remes, Markus Heinonen, Samuel Kaski
Neural Non-Stationary Spectral Kernel. CoRR (2018)
Pashupati Hegde, Markus Heinonen, Samuel Kaski
Variational zero-inflated Gaussian processes with sparse kernels. UAI (2018)
Anna Cichonska, Tapio Pahikkala, Sándor Szedmák, Heli Julkunen, Antti Airola, Markus Heinonen, Tero Aittokallio, Juho Rousu
Learning with multiple pairwise kernels for drug bioactivity prediction. Bioinform. 34 (13) (2018)
Emmi Jokinen, Markus Heinonen, Harri Lähdesmäki
mGPfusion: predicting protein stability changes with Gaussian process kernel learning and data fusion. Bioinform. 34 (13) (2018)
Pashupati Hegde, Markus Heinonen, Harri Lähdesmäki, Samuel Kaski
Deep learning with differential Gaussian process flows. CoRR (2018)
Markus Heinonen, Maria Osmala, Henrik Mannerström, Janne Wallenius, Samuel Kaski, Juho Rousu, Harri Lähdesmäki
Bayesian Metabolic Flux Analysis reveals intracellular flux couplings. CoRR (2018)
Kenneth Blomqvist, Samuel Kaski, Markus Heinonen
Deep convolutional Gaussian processes. CoRR (2018)
Zheyang Shen, Markus Heinonen, Samuel Kaski
Harmonizable mixture kernels with variational Fourier features. CoRR (2018)
Çagatay Yildiz, Markus Heinonen, Jukka Intosalmi, Henrik Mannerström, Harri Lähdesmäki
Learning stochastic differential equations with Gaussian Processes without Gradient Matching. MLSP (2018)
Markus Heinonen, Çagatay Yildiz, Henrik Mannerström, Jukka Intosalmi, Harri Lähdesmäki
Learning unknown ODE models with Gaussian processes. ICML (2018)
Çagatay Yildiz, Markus Heinonen, Jukka Intosalmi, Henrik Mannerström, Harri Lähdesmäki
Learning Stochastic Differential Equations With Gaussian Processes Without Gradient Matching. CoRR (2018)
Sami Remes, Markus Heinonen, Samuel Kaski
Non-Stationary Spectral Kernels. CoRR (2017)
Sami Remes, Markus Heinonen, Samuel Kaski
A Mutually-Dependent Hadamard Kernel for Modelling Latent Variable Couplings. ACML (2017)
Sami Remes, Markus Heinonen, Samuel Kaski
Non-Stationary Spectral Kernels. NIPS (2017)
Markus Heinonen, Henrik Mannerström, Juho Rousu, Samuel Kaski, Harri Lähdesmäki
Non-Stationary Gaussian Process Regression with Hamiltonian Monte Carlo. AISTATS (2016)
Romain Brault, Markus Heinonen, Florence d'Alché-Buc
Random Fourier Features For Operator-Valued Kernels. ACML (2016)
Romain Brault, Florence d'Alché-Buc, Markus Heinonen
Random Fourier Features for Operator-Valued Kernels. CoRR (2016)
Markus Heinonen, Olivier Guipaud, Fabien Milliat, Valérie Buard, Béatrice Micheau, Georges Tarlet, Marc Benderitter, Farida Zehraoui, Florence d'Alché-Buc
Detecting time periods of differential gene expression using Gaussian processes: an application to endothelial cells exposed to radiotherapy dose fraction. Bioinform. 31 (5) (2015)
Markus Heinonen, Florence d'Alché-Buc
Learning nonparametric differential equations with operator-valued kernels and gradient matching. CoRR (2014)
Markus Heinonen, Niko Välimäki, Veli Mäkinen, Juho Rousu
Efficient Path Kernels for Reaction Function Prediction. BIOINFORMATICS (2012)
Markus Heinonen, Huibin Shen, Nicola Zamboni, Juho Rousu
Metabolite identification and molecular fingerprint prediction through machine learning. Bioinform. 28 (18) (2012)
Markus Heinonen, Sampsa Lappalainen, Taneli Mielikäinen, Juho Rousu
Computing Atom Mappings for Biochemical Reactions without Subgraph Isomorphism. J. Comput. Biol. 18 (1) (2011)
Hongyu Su, Markus Heinonen, Juho Rousu
Structured Output Prediction of Anti-cancer Drug Activity. PRIB (2010)
Markus Heinonen, Ari Rantanen, Taneli Mielikäinen, Esa Pitkänen, Juha Kokkonen, Juho Rousu
Ab Initio Prediction of Molecular Fragments from Tandem Mass Spectrometry Data. German Conference on Bioinformatics (2006)