Ab Initio Prediction of Molecular Fragments from Tandem Mass Spectrometry Data. - Markus Heinonen; Ari Rantanen; Taneli Mielikäinen; Esa Pitkänen; Juha Kokkonen; Juho Rousu (German Conference on Bioinformatics, 2006)

Structured Output Prediction of Anti-cancer Drug Activity. - Hongyu Su; Markus Heinonen; Juho Rousu (PRIB, 2010)

Computing Atom Mappings for Biochemical Reactions without Subgraph Isomorphism. - Markus Heinonen; Sampsa Lappalainen; Taneli Mielikäinen; Juho Rousu (J. Comput. Biol., 2011)

Efficient Path Kernels for Reaction Function Prediction. - Markus Heinonen; Niko Välimäki; Veli Mäkinen; Juho Rousu (BIOINFORMATICS, 2012)

Metabolite identification and molecular fingerprint prediction through machine learning. - Markus Heinonen; Huibin Shen; Nicola Zamboni; Juho Rousu (Bioinform., 2012)

Computational methods for small molecules. - Markus Heinonen (2012)

Learning nonparametric differential equations with operator-valued kernels and gradient matching. - Markus Heinonen; Florence d'Alché-Buc (CoRR, 2014)

Detecting time periods of differential gene expression using Gaussian processes: an application to endothelial cells exposed to radiotherapy dose fraction. - Markus Heinonen; Olivier Guipaud; Fabien Milliat; Valérie Buard; Béatrice Micheau; Georges Tarlet; Marc Benderitter; Farida Zehraoui; Florence d'Alché-Buc (Bioinform., 2015)

Random Fourier Features For Operator-Valued Kernels. - Romain Brault; Markus Heinonen; Florence d'Alché-Buc (ACML, 2016)

Non-Stationary Gaussian Process Regression with Hamiltonian Monte Carlo. - Markus Heinonen; Henrik Mannerström; Juho Rousu; Samuel Kaski; Harri Lähdesmäki (AISTATS, 2016)

A Mutually-Dependent Hadamard Kernel for Modelling Latent Variable Couplings. - Sami Remes; Markus Heinonen; Samuel Kaski (ACML, 2017)

Non-Stationary Spectral Kernels. - Sami Remes; Markus Heinonen; Samuel Kaski (NIPS, 2017)

Learning unknown ODE models with Gaussian processes. - Markus Heinonen; Çagatay Yildiz; Henrik Mannerström; Jukka Intosalmi; Harri Lähdesmäki (ICML, 2018)

Learning stochastic differential equations with Gaussian Processes without Gradient Matching. - Çagatay Yildiz; Markus Heinonen; Jukka Intosalmi; Henrik Mannerström; Harri Lähdesmäki (MLSP, 2018)

Variational zero-inflated Gaussian processes with sparse kernels. - Pashupati Hegde; Markus Heinonen; Samuel Kaski (UAI, 2018)

Learning with multiple pairwise kernels for drug bioactivity prediction. - Anna Cichonska; Tapio Pahikkala; Sándor Szedmák; Heli Julkunen; Antti Airola; Markus Heinonen; Tero Aittokallio; Juho Rousu (Bioinform., 2018)

mGPfusion: predicting protein stability changes with Gaussian process kernel learning and data fusion. - Emmi Jokinen; Markus Heinonen; Harri Lähdesmäki (Bioinform., 2018)

Neural Non-Stationary Spectral Kernel. - Sami Remes; Markus Heinonen; Samuel Kaski (CoRR, 2018)

Deep learning with differential Gaussian process flows. - Pashupati Hegde; Markus Heinonen; Harri Lähdesmäki; Samuel Kaski (AISTATS, 2019)

Harmonizable mixture kernels with variational Fourier features. - Zheyang Shen; Markus Heinonen; Samuel Kaski (AISTATS, 2019)

ODE2VAE: Deep generative second order ODEs with Bayesian neural networks. - Çagatay Yildiz; Markus Heinonen; Harri Lähdesmäki (NeurIPS, 2019)

Deep Convolutional Gaussian Processes. - Kenneth Blomqvist; Samuel Kaski; Markus Heinonen (ECML/PKDD (2), 2019)

Bayesian metabolic flux analysis reveals intracellular flux couplings. - Markus Heinonen; Maria Osmala; Henrik Mannerström; Janne Wallenius; Samuel Kaski; Juho Rousu; Harri Lähdesmäki (Bioinform., 2019)

ODE$^2$VAE: Deep generative second order ODEs with Bayesian neural networks. - Çagatay Yildiz; Markus Heinonen; Harri Lähdesmäki (CoRR, 2019)

Learning spectrograms with convolutional spectral kernels. - Zheyang Shen; Markus Heinonen; Samuel Kaski (AISTATS, 2020)

Rethinking Sparse Gaussian Processes: Bayesian Approaches to Inducing-Variable Approximations. - Simone Rossi 001; Markus Heinonen; Edwin V. Bonilla; Zheyang Shen; Maurizio Filippone (CoRR, 2020)

Scalable Bayesian neural networks by layer-wise input augmentation. - Trung Q. Trinh; Samuel Kaski; Markus Heinonen (CoRR, 2020)

Sample-efficient reinforcement learning using deep Gaussian processes. - Charles W. L. Gadd; Markus Heinonen; Harri Lähdesmäki; Samuel Kaski (CoRR, 2020)

Bayesian Inference for Optimal Transport with Stochastic Cost. - Anton Mallasto; Markus Heinonen; Samuel Kaski (ACML, 2021)

Sparse Gaussian Processes Revisited: Bayesian Approaches to Inducing-Variable Approximations. - Simone Rossi 001; Markus Heinonen; Edwin V. Bonilla; Zheyang Shen; Maurizio Filippone (AISTATS, 2021)

Learning continuous-time PDEs from sparse data with graph neural networks. - Valerii Iakovlev; Markus Heinonen; Harri Lähdesmäki (ICLR, 2021)

Continuous-time Model-based Reinforcement Learning. - Çagatay Yildiz; Markus Heinonen; Harri Lähdesmäki (ICML, 2021)

De-randomizing MCMC dynamics with the diffusion Stein operator. - Zheyang Shen; Markus Heinonen; Samuel Kaski (NeurIPS, 2021)

Affine Transport for Sim-to-Real Domain Adaptation. - Anton Mallasto; Karol Arndt; Markus Heinonen; Samuel Kaski; Ville Kyrki (CoRR, 2021)

Bayesian inference of ODEs with Gaussian processes. - Pashupati Hegde; Çagatay Yildiz; Harri Lähdesmäki; Samuel Kaski; Markus Heinonen (CoRR, 2021)

Evolving-Graph Gaussian Processes. - David Blanco Mulero; Markus Heinonen; Ville Kyrki (CoRR, 2021)

Predicting recognition between T cell receptors and epitopes with TCRGP. - Emmi Jokinen; Jani Huuhtanen; Satu Mustjoki; Markus Heinonen; Harri Lähdesmäki (PLoS Comput. Biol., 2021)

Tackling covariate shift with node-based Bayesian neural networks. - Trung Q. Trinh; Markus Heinonen; Luigi Acerbi; Samuel Kaski (ICML, 2022)

Modular Flows: Differential Molecular Generation. - Yogesh Verma; Samuel Kaski; Markus Heinonen; Vikas Garg 001 (NeurIPS, 2022)

Variational multiple shooting for Bayesian ODEs with Gaussian processes. - Pashupati Hegde; Çagatay Yildiz; Harri Lähdesmäki; Samuel Kaski; Markus Heinonen (UAI, 2022)

Look beyond labels: Incorporating functional summary information in Bayesian neural networks. - Vishnu Raj; Tianyu Cui; Markus Heinonen; Pekka Marttinen (CoRR, 2022)

Likelihood-free inference with deep Gaussian processes. - Alexander Aushev; Henri Pesonen; Markus Heinonen; Jukka Corander; Samuel Kaski (Comput. Stat. Data Anal., 2022)

Differential Equations and Continuous-Time Deep Learning (Dagstuhl Seminar 22332). - David Duvenaud; Markus Heinonen; Michael Tiemann 001; Max Welling (Dagstuhl Reports, 2022)

Human-in-the-loop assisted de novo molecular design. - Iiris Sundin; Alexey Voronov; Haoping Xiao; Kostas Papadopoulos; Esben Jannik Bjerrum; Markus Heinonen; Atanas Patronov; Samuel Kaski; Ola Engkvist (J. Cheminformatics, 2022)

Modeling binding specificities of transcription factor pairs with random forests. - Anni A. Antikainen; Markus Heinonen; Harri Lähdesmäki (BMC Bioinform., 2022)

Incorporating functional summary information in Bayesian neural networks using a Dirichlet process likelihood approach. - Vishnu Raj; Tianyu Cui; Markus Heinonen; Pekka Marttinen (AISTATS, 2023)

Latent Neural ODEs with Sparse Bayesian Multiple Shooting. - Valerii Iakovlev; Çagatay Yildiz; Markus Heinonen; Harri Lähdesmäki (ICLR, 2023)

Generative Modelling with Inverse Heat Dissipation. - Severi Rissanen; Markus Heinonen; Arno Solin (ICLR, 2023)

AbODE: Ab initio antibody design using conjoined ODEs. - Yogesh Verma; Markus Heinonen; Vikas Garg 001 (ICML, 2023)

Continuous-Time Functional Diffusion Processes. - Giulio Franzese; Giulio Corallo; Simone Rossi 001; Markus Heinonen; Maurizio Filippone; Pietro Michiardi (NeurIPS, 2023)

Learning Space-Time Continuous Latent Neural PDEs from Partially Observed States. - Valerii Iakovlev; Markus Heinonen; Harri Lähdesmäki (NeurIPS, 2023)

Uncertainty-Aware Natural Language Inference with Stochastic Weight Averaging. - Aarne Talman; Hande Çelikkanat; Sami Virpioja; Markus Heinonen; Jörg Tiedemann (NoDaLiDa, 2023)

Continuous-Time Functional Diffusion Processes. - Giulio Franzese; Simone Rossi 001; Dario Rossi 001; Markus Heinonen; Maurizio Filippone; Pietro Michiardi (CoRR, 2023)

Beyond invariant representation learning: linearly alignable latent spaces for efficient closed-form domain adaptation. - Oliver Struckmeier; Ievgen Redko; Anton Mallasto; Karol Arndt; Markus Heinonen; Ville Kyrki (CoRR, 2023)

Input gradient diversity for neural network ensembles. - Trung Q. Trinh; Markus Heinonen; Luigi Acerbi; Samuel Kaski (CoRR, 2023)

Learning Space-Time Continuous Neural PDEs from Partially Observed States. - Valerii Iakovlev; Markus Heinonen; Harri Lähdesmäki (CoRR, 2023)

Understanding deep neural networks through the lens of their non-linearity. - Quentin Bouniot; Ievgen Redko; Anton Mallasto; Charlotte Laclau; Karol Arndt; Oliver Struckmeier; Markus Heinonen; Ville Kyrki; Samuel Kaski (CoRR, 2023)

Learning Energy Conserving Dynamics Efficiently with Hamiltonian Gaussian Processes. - Magnus Ross; Markus Heinonen (Trans. Mach. Learn. Res., 2023)

Learning representations that are closed-form Monge mapping optimal with application to domain adaptation. - Oliver Struckmeier; Ievgen Redko; Anton Mallasto; Karol Arndt; Markus Heinonen; Ville Kyrki (Trans. Mach. Learn. Res., 2023)

TCRconv: predicting recognition between T cell receptors and epitopes using contextualized motifs. - Emmi Jokinen; Alexandru Dumitrescu; Jani Huuhtanen; Vladimir Gligorijevic; Satu Mustjoki; Richard Bonneau; Markus Heinonen; Harri Lähdesmäki (Bioinform., 2023)

Balancing Imbalanced Toxicity Models: Using MolBERT with Focal Loss. - Muhammad Arslan Masood; Samuel Kaski; Hugo Ceulemans; Dorota Herman; Markus Heinonen (AIDD@ICANN, 2024)

Towards Interpretable Models of Chemist Preferences for Human-in-the-Loop Assisted Drug Discovery. - Yasmine Nahal; Markus Heinonen; Mikhail Kabeshov; Jon Paul Janet; Eva Nittinger; Ola Engkvist; Samuel Kaski (AIDD@ICANN, 2024)

Input-gradient space particle inference for neural network ensembles. - Trung Q. Trinh; Markus Heinonen; Luigi Acerbi; Samuel Kaski (ICLR, 2024)

ClimODE: Climate and Weather Forecasting with Physics-informed Neural ODEs. - Yogesh Verma; Markus Heinonen; Vikas Garg 001 (ICLR, 2024)

Improving robustness to corruptions with multiplicative weight perturbations. - Trung Q. Trinh; Markus Heinonen; Luigi Acerbi; Samuel Kaski (NeurIPS, 2024)

Field-based Molecule Generation. - Alexandru Dumitrescu; Dani Korpela; Markus Heinonen; Yogesh Verma; Valerii Iakovlev; Vikas Garg 001; Harri Lähdesmäki (CoRR, 2024)

Alignment is Key for Applying Diffusion Models to Retrosynthesis. - Najwa Laabid; Severi Rissanen; Markus Heinonen; Arno Solin; Vikas Garg 001 (CoRR, 2024)

Improving Discrete Diffusion Models via Structured Preferential Generation. - Severi Rissanen; Markus Heinonen; Arno Solin (CoRR, 2024)

Improving robustness to corruptions with multiplicative weight perturbations. - Trung Trinh; Markus Heinonen; Luigi Acerbi; Samuel Kaski (CoRR, 2024)

What Ails Generative Structure-based Drug Design: Too Little or Too Much Expressivity? - Rafal Karczewski; Samuel Kaski; Markus Heinonen; Vikas Garg 001 (CoRR, 2024)

Human-in-the-loop active learning for goal-oriented molecule generation. - Yasmine Nahal; Janosch Menke; Julien Martinelli; Markus Heinonen; Mikhail Kabeshov; Jon Paul Janet; Eva Nittinger; Ola Engkvist; Samuel Kaski (J. Cheminformatics, 2024)

Robust Classification by Coupling Data Mollification with Label Smoothing. - Markus Heinonen; Ba-Hien Tran; Michael Kampffmeyer; Maurizio Filippone (AISTATS, 2025)

What Ails Generative Structure-based Drug Design: Expressivity is Too Little or Too Much? - Rafal Karczewski; Samuel Kaski; Markus Heinonen; Vikas K. Garg 001 (AISTATS, 2025)

From Alexnet to Transformers: Measuring the Non-linearity of Deep Neural Networks with Affine Optimal Transport. - Quentin Bouniot; Ievgen Redko; Anton Mallasto; Charlotte Laclau; Oliver Struckmeier; Karol Arndt; Markus Heinonen; Ville Kyrki; Samuel Kaski (CVPR, 2025)

E(3)-equivariant models cannot learn chirality: Field-based molecular generation. - Alexandru Dumitrescu; Dani Korpela; Markus Heinonen; Yogesh Verma; Valerii Iakovlev; Vikas Garg 001; Harri Lähdesmäki (ICLR, 2025)

Diffusion Models as Cartoonists: The Curious Case of High Density Regions. - Rafal Karczewski; Markus Heinonen; Vikas Garg 001 (ICLR, 2025)

Equivariant Denoisers Cannot Copy Graphs: Align Your Graph Diffusion Models. - Najwa Laabid; Severi Rissanen; Markus Heinonen; Arno Solin; Vikas Garg 001 (ICLR, 2025)

Free Hunch: Denoiser Covariance Estimation for Diffusion Models Without Extra Costs. - Severi Rissanen; Markus Heinonen; Arno Solin (ICLR, 2025)

Gaussian Processes with Bayesian Inference of Covariate Couplings. - Mattia Rosso; Juho Ylä-Jääski; Zheyang Shen; Markus Heinonen; Maurizio Filippone (Trans. Mach. Learn. Res., 2025)

Devil is in the Details: Density Guidance for Detail-Aware Generation with Flow Models. - Rafal Karczewski; Markus Heinonen; Vikas Garg 001 (CoRR, 2025)

Repulsive Ensembles for Bayesian Inference in Physics-informed Neural Networks. - Philipp Pilar; Markus Heinonen; Niklas Wahlström (CoRR, 2025)

Spacetime Geometry of Denoising in Diffusion Models. - Rafal Karczewski; Markus Heinonen; Alison Pouplin; Søren Hauberg; Vikas Garg 001 (CoRR, 2025)

Optimizing Data Augmentation through Bayesian Model Selection. - Madi Matymov; Ba-Hien Tran; Michael Kampffmeyer; Markus Heinonen; Maurizio Filippone (CoRR, 2025)

Progressive Tempering Sampler with Diffusion. - Severi Rissanen; Ruikang Ouyang; Jiajun He 003; Wenlin Chen; Markus Heinonen; Arno Solin; José Miguel Hernández-Lobato (CoRR, 2025)

Scaling Laws for Uncertainty in Deep Learning. - Mattia Rosso; Simone Rossi 001; Giulio Franzese; Markus Heinonen; Maurizio Filippone (CoRR, 2025)

VitroBert: modeling DILI by pretraining BERT on in vitro data. - Muhammad Arslan Masood; Anamya Ajjolli Nagaraja; Katia Belaid; Natalie Mesens; Hugo Ceulemans; Samuel Kaski; Dorota Herman; Markus Heinonen (J. Cheminformatics, 2025)