Publications

preprints
• Devil is in the Details: Density Guidance for Detail-Aware Generation with Flow Models
  Rafal Karczewski, Markus Heinonen, Vikas Garg
  arXiv (2025)  
• Gaussian Processes with Bayesian Inference of Covariate Couplings
  Mattia Rosso, Juho Ylä-Jääski, Zheyang Shen, Markus Heinonen, Maurizio Filippone
  OpenReview (2025)
2025
• 64. From Alexnet to Transformers: Measuring the Non-linearity of Deep Neural Networks with Affine Optimal Transport
  Quentin Bouniot, Ievgen Redko, Anton Mallasto, Charlotte Laclau, Karol Arndt, Oliver Struckmeier, Markus Heinonen, Ville Kyrki, Samuel Kaski
  CVPR (2025)  
• 63. Diffusion Models as Cartoonists! The Curious Case of High Density Regions
  Rafal Karczewski, Markus Heinonen, Vikas Garg
  ICLR (2025)    
• 62. Free Hunch: Denoiser Covariance Estimation for Diffusion Models Without Extra Costs
  Severi Rissanen, Markus Heinonen, Arno Solin
  ICLR (2025)  
• 61. Equivariant Denoisers Cannot Copy Graphs: Align Your Graph Diffusion Models
  Najwa Laabid, Severi Rissanen, Markus Heinonen, Arno Solin, Vikas Garg
  ICLR (2025)  
• 60. E(3)-equivariant models cannot learn chirality: Field-based molecular generation
  Alexandru Dumitrescu, Dani Korpela, Markus Heinonen, Yogesh Verma, Valerii Iakovlev, Vikas Garg, Harri Lähdesmäki
  ICLR (2025)  
• 59. What Ails Generative Structure-based Drug Design: Too Little or Too Much Expressivity?
  Rafał Karczewski, Samuel Kaski, Markus Heinonen, Vikas Garg
  AISTATS (2025)     Oral presentation (top 2%)
• 58. Robust Classification by Coupling Data Mollification with Label Smoothing
  Markus Heinonen, Ba-Hien Tran, Michael Kampffmeyer, Maurizio Filippone
  AISTATS (2025)  
2024
• 57. Probabilistic analysis of spatial viscoelastic cues in 3D cell culture using magnetic microrheometry
  Ossi Arasalo, Arttu Lehtonen, Mari Kielosto, Markus Heinonen, Juho Pokki
  Biophysical J (2024)  
• 56. 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 Chemoinformatics (2024)  
• 55. Improving robustness to corruptions with multiplicative weight perturbations
  Trung Trinh, Markus Heinonen, Luigi Acerbi, Samuel Kaski
  NeurIPS (2024)         Spotlight presentation (top 8%)
• 54. ClimODE: Climate Forecasting With Physics-informed Neural ODEs
  Yogesh Verma, Markus Heinonen, Vikas Garg
  ICLR (2024)         Oral presentation (top 4%)
• 53. Input-gradient space particle inference for neural network ensembles
  Trung Trinh, Markus Heinonen, Luigi Acerbi, Samuel Kaski
  ICLR (2024)         Spotlight presentation (top 16%)
2023
• 52. Learning Space-Time Continuous Latent Neural PDEs from Partially Observed States
  Valerii Iakovlev, Markus Heinonen, Harri Lähdesmäki
  NeurIPS (2023)    
• 51. Continuous-Time Functional Diffusion Processes
  Giulio Franzese, Giulio Corallo, Simone Rossi, Markus Heinonen, Maurizio Filippone, Pietro Michiardi
  NeurIPS (2023)    
• 50. Chemistry-based modelling on phenotype-based drug-induced liver injury annotation: from public to proprietary data
  Mohammad Moein, Markus Heinonen, Natalie Mesens, Ronnie Chamanza, Chidozie Amuzie, Yvonne Will, Hugo Ceulemans, Samuel Kaski, Dorota Herman
  Chemical Research in Toxicology (2023)
• 49. 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
  TMLR (2023)  
• 48. Uncertainty-Aware Natural Language Inference with Stochastic Weight Averaging
  Aarne Talman, Hande Celikkanat, Sami Virpioja, Markus Heinonen, Jörg Tiedemann
  NoDaLiDa (2023)  
• 47. AbODE: Ab initio antibody design using conjoined ODEs
  Yogesh Verma, Markus Heinonen, Vikas Garg
  ICML (2023)      
• 46. Learning Energy Conserving Dynamics Efficiently with Hamiltonian Gaussian Processes
  Magnus Ross, Markus Heinonen
  TMLR (2023)    
• 45. Incorporating functional summary information in Bayesian neural networks using a Dirichlet process likelihood approach
  Vishnu Raj, Tianyu Cui, Markus Heinonen, Pekka Marttinen
  AISTATS (2023)    
• 44. Latent Neural ODEs with Sparse Bayesian Multiple Shooting
  Valerii Iakovlev, Cagatay Yildiz, Markus Heinonen, Harri Lähdesmäki
  ICLR (2023)  
  
• 43. Generative Modelling With Inverse Heat Dissipation
  Severi Rissanen, Markus Heinonen, Arno Solin
  ICLR (2023)      
2022
• 42. Human-in-the-loop assisted de novo molecular design
  Iiris Sundin, Alexey Voronov, Haoping Xiao, Kostas Papadopoulos, Esben Bjerrum, Markus Heinonen, Atanas Patronov, Samuel Kaski, Ola Engkvist
  J Chemoinformatics (2022)  
  
• 41. 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
  Bioinformatics (2022)  
  
• 40. Modular Flows: Differential Molecular Generation
  Yogesh Verma, Samuel Kaski, Markus Heinonen, Vikas Garg
  NeurIPS (2022)      
• 39. Tackling covariate shift with node-based Bayesian neural networks
  Trung Trinh, Markus Heinonen, Luigi Acerbi, Samuel Kaski
  ICML (2022)         Oral presentation (top 10%)
• 38. Variational multiple shooting for Bayesian ODEs with Gaussian processes
  Pashupati Hegde, Cagatay Yildiz, Harri Lähdesmäki, Samuel Kaski, Markus Heinonen
  UAI (2022)  
• 37. Likelihood-Free Inference with Deep Gaussian Processes
  Alexander Aushev, Henri Pesonen, Markus Heinonen, Jukka Corander, Samuel Kaski
  Computational Statistics and Data Analysis (2022)  
• 36. Modeling binding specificities of transcription factor pairs with random forests
  Anni Antikainen, Markus Heinonen, Harri Lähdesmäki
  BMC Bioinformatics (2022)  
2021
• 35. Prediction and impact of personalized donation intervals
  Jarkko Toivonen, Yrjö Koski, Esa Turkulainen, Femmeke Prinsze, Pietro della Briotta Parolo, Markus Heinonen, Mikko Arvas
  Vox Sanguinis (2021)  
• 34. De-randomizing MCMC dynamics with the diffusion Stein operator
  Zheyang Shen, Markus Heinonen, Samuel Kaski
  NeurIPS (2021)  
• 33. Bayesian Inference for Optimal Transport with Stochastic Cost
  Anton Mallasto, Markus Heinonen, Samuel Kaski
  ACML (2021)
  
• 32. Continuous-Time Model-Based Reinforcement Learning
  Çağatay Yıldız, Markus Heinonen, Harri Lähdesmäki
  ICML (2021)  
  
• 31. Predicting recognition between T cell receptors and epitopes with TCRGP
  Emmi Jokinen, Markus Heinonen, Jani Huuhtanen, Satu Mustjoki, Harri Lähdesmäki
  PLOS Computational Biology (2021)  
• 30. Learning continuous-time PDEs from sparse data with graph neural networks
  Valerii Iakovlev, Markus Heinonen, Harri Lähdesmäki
  ICLR (2021)  
  
• 29. Sparse Gaussian Processes Revisited: Bayesian Approaches to Inducing-Variable Approximations
  Simone Rossi, Markus Heinonen, Edwin Bonilla, Zheyang Shen, Maurizio Filippone
  AISTATS (2021)  
  
2020
• 28. Substrate specificity of 2-Deoxy-D-ribose 5-phosphate aldolase (DERA) assessed by different protein engineering and machine learning methods
  Sanni Voutilainen, Markus Heinonen, Martina Andberg, Emmi Jokinen, Hannu Maaheimo, Johan Pääkkönen, Nina Hakulinen, Juha Rouvinen, Harri Lähdesmäki, Samuel Kaski, Juho Rousu, Merja Penttilä, Anu Koivula
  Applied Microbiology and Biotechnology (2020)
  
• Sample-efficient reinforcement learning using deep Gaussian processes
  Charles Gadd, Markus Heinonen, Harri Lähdesmäki, Samuel Kaski
  Technical report (2020)
  
• Scalable Bayesian neural networks by layer-wise input augmentation
  Trung Trinh, Samuel Kaski, Markus Heinonen
  Technical report (2020)  
  
• 27. Learning spectrograms with convolutional spectral kernels
  Zheyang Shen, Markus Heinonen, Samuel Kaski
  AISTATS (2020)
  
2019
• 26. ODE²VAE: Deep generative second order ODEs with Bayesian neural networks
  Cagatay Yildiz, Markus Heinonen, Harri Lähdesmäki
  NIPS (2019)      
• 25. Deep Convolutional Gaussian Processes
  Kenneth Blomqvist, Samuel Kaski, Markus Heinonen
  ECML/PKDD (2019)  
• 24. Bayesian Metabolic Flux Analysis reveals intracellular flux couplings
  Markus Heinonen, Maria Osmala, Henrik Mannerström, Janne Wallenius, Samuel Kaski, Juho Rousu, Harri Lähdesmäki
  ISMB/ECCB (2019)  
• 23. Deep learning with differential Gaussian process flows
  Pashupati Hegde, Markus Heinonen, Harri Lähdesmäki, Samuel Kaski
  AISTATS (2019)       Notable paper award (top 1%)
• 22. Harmonizable mixture kernels with variational Fourier features
  Zheyang Shen, Markus Heinonen, Samuel Kaski
  AISTATS (2019)
2018
• 21. Learning Stochastic Differential Equations With Gaussian Processes Without Gradient Matching
  Cagatay Yildiz, Markus Heinonen, Jukka Intosalmi, Henrik Mannerström, Harri Lähdesmäki
  MLSP (2018)  
• 20. Learning unknown ODE models with Gaussian processes
  Markus Heinonen, Cagatay Yildiz, Henrik Mannerström, Jukka Intosalmi, Harri Lähdesmäki
  ICML (2018)    
• 19. Variational zero-inflated Gaussian processes with sparse kernels
  Pashupati Hegde, Markus Heinonen, Samuel Kaski
  UAI (2018)   ZIGP   GPRN
• Neural Non-Stationary Spectral Kernel
  Sami Remes, Markus Heinonen, Samuel Kaski
  Technical report (2018)
• 18. Temporal clustering analysis of endothelial cell gene expression following exposure to a conventional radiotherapy dose fraction using Gaussian process clustering
  Markus Heinonen, Fabien Milliat, Mohamed Benadjaoud, Agnès François, Valérie Buard, Georges Tarlet, Florence d’Alché-Buc, Olivier Guipaud
  PLOS ONE (2018)
• 17. Learning with multiple pairwise kernels for drug bioactivity prediction
  Anna Cichonska, Tapio Pahikkala, Sandor Szedmak, Heli Julkunen, Antti Airola, Markus Heinonen, Tero Aittokallio, Juho Rousu
  Bioinformatics (2018)  
• 16. mGPfusion: Predicting protein stability changes with Gaussian process kernel learning and data fusion
  Emmi Jokinen, Markus Heinonen, Harri Lähdesmäki
  Bioinformatics (2018)  
• 15. Flex ddG: Rosetta Ensemble-Based Estimation of Changes in Protein-Protein Binding Affinity Upon Mutation
  Kyle Barlow, Shane Conchuir, Samuel Thompson, Pooja Suresh, James Lucas, Markus Heinonen, Tanja Kortemme
  J Physical Chemistry B (2018)
2017
• 14. A Mutually-Dependent Hadamard Kernel for Modelling Latent Variable Couplings
  Sami Remes, Markus Heinonen, Samuel Kaski
  ACML (2017)  
• 13. Non-Stationary Spectral Kernels
  Sami Remes, Markus Heinonen, Samuel Kaski
  NIPS (2017)  
2016
• 12. Random Fourier Features for operator-valued kernels
  Romain Brault, Markus Heinonen, Florence d'Alche-Buc
  ACML (2016)  
• 11. Non-Stationary Gaussian Process Regression with Hamiltonian Monte Carlo
  Markus Heinonen, Henrik Mannerström, Juho Rousu, Samuel Kaski, Harri Lähdesmäki
  AISTATS (2016)  
• 10. Genome wide analysis of protein production load in Trichoderma reesei
  Tiina Pakula, Heli Nygren, Dorothee Barth, Markus Heinonen, Sandra Castillo, Merja Penttilä, Mikko Arvas
  Biotechnology for Biofuels (2016)
2015
• 9. 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, Valerie Buard, Beatrice Micheau, Georges Tarlet, Marc Benderittter, Farida Zehraoui, Florence d'Alche-Buc
  Bioinformatics (2015)  
2014
• Learning nonparametric differential equations with operator-valued kernels and gradient matching
  Markus Heinonen, Florence d'Alche-Buc
  Technical report (2014)
2013
• Time-dependent gaussian process regression and significance analysis for sparse time-series
  Markus Heinonen, Olivier Guipaud, Fabien Milliat, Valerie Buard, Beatrice Micheau, Florence d'Alche-Buc
  Machine Learning in Systems Biology (2013)
• 8. Metabolite Identification trough Machine Learning -- Tackling CASMI Challenge using FingerID
  Huibin Shen, Nicola Zamboni, Markus Heinonen, Juho Rousu
  Metabolites (2013)
2012
• Full waveform forward seismic modeling of geologically complex environment: Comparison of simulated and field seismic data
  Suvi Heinonen, Markus Heinonen and Emilia Koivisto
  EGU General Assembly (2012)
• 7. Metabolite identification and fingerprint prediction via machine learning
  Markus Heinonen, Huibin Shen, Nicola Zamboni, Juho Rousu
  Bioinformatics (2012)
• 6. Efficient path kernels for reaction function prediction
  Markus Heinonen, Niko Välimäki, Veli Mäkinen, Juho Rousu
  International Conference on Bioinformatics Models, Methods and Algorithms (2012)
  
2011
• 5. Computing atom mappings for biochemical reactions without subgraphs isomorphism
  Markus Heinonen, Sampsa Lappalainen, Taneli Mielikäinen, Juho Rousu
  J Computational Biology (2011)   
2010
• 4. Structured output prediction of anti-cancer drug activity
  Hongyu Su, Markus Heinonen, Juho Rousu
  Pattern Recognition in Bioinformatics (2010)
  
• 3. Multilabel Classification of Drug-like Molecules via Max-margin Conditional Random Fields
  Hongyu Su, Markus Heinonen, Juho Rousu
  Probabilistic Graphical Models (2010)
  
2008
• 2. FiD: a software for ab initio structural identification of product ions from tandem mass spectrometric data
  Markus Heinonen, Ari Rantanen, Taneli Mielikäinen, Juha Kokkonen, Jari Kiuru, Raimo Ketola, Juho Rousu
  Rapid Communications in Mass Spectrometry (2008)
  
2006
• 1. 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)