Markus Heinonen

115 papers A* 22A 10C 3Journal 70Unranked 9

Year	Rank	Type	Title / Venue / Authors
2025	A*	`conf`	Devil is in the Details: Density Guidance for Detail-Aware Generation with Flow Models. ICML Rafal Karczewski, Markus Heinonen, Vikas K. Garg
2025	J	`jnl`	Devil is in the Details: Density Guidance for Detail-Aware Generation with Flow Models. CoRR Rafal Karczewski, Markus Heinonen, Vikas Garg
2025	A*	`conf`	Diffusion Models as Cartoonists: The Curious Case of High Density Regions. ICLR Rafal Karczewski, Markus Heinonen, Vikas Garg
2025	A*	`conf`	E(3)-equivariant models cannot learn chirality: Field-based molecular generation. ICLR Alexandru Dumitrescu, Dani Korpela, Markus Heinonen, Yogesh Verma, Valerii Iakovlev, Vikas Garg, Harri Lähdesmäki
2025	A*	`conf`	Equivariant Denoisers Cannot Copy Graphs: Align Your Graph Diffusion Models. ICLR Najwa Laabid, Severi Rissanen, Markus Heinonen, Arno Solin, Vikas Garg
2025	A*	`conf`	Free Hunch: Denoiser Covariance Estimation for Diffusion Models Without Extra Costs. ICLR Severi Rissanen, Markus Heinonen, Arno Solin
2025	A*	`conf`	From Alexnet to Transformers: Measuring the Non-linearity of Deep Neural Networks with Affine Optimal Transport. CVPR Quentin Bouniot, Ievgen Redko, Anton Mallasto, Charlotte Laclau, Oliver Struckmeier, Karol Arndt, Markus Heinonen, Ville Kyrki, Samuel Kaski
2025	J	`jnl`	Gaussian Processes with Bayesian Inference of Covariate Couplings. Trans. Mach. Learn. Res. Mattia Rosso, Juho Ylä-Jääski, Zheyang Shen, Markus Heinonen, Maurizio Filippone
2025	J	`jnl`	Let Physics Guide Your Protein Flows: Topology-aware Unfolding and Generation. CoRR Yogesh Verma, Markus Heinonen, Vikas Garg
2025	J	`jnl`	Multi-target property prediction and optimization using latent spaces of generative model. Mach. Learn. Sci. Technol. Anirudh Jain, Markus Heinonen, Heikki Käsnänen, Julius Sipilä, Samuel Kaski
2025	J	`jnl`	Optimizing Data Augmentation through Bayesian Model Selection. CoRR Madi Matymov, Ba-Hien Tran, Michael Kampffmeyer, Markus Heinonen, Maurizio Filippone
2025	J	`jnl`	PriorGuide: Test-Time Prior Adaptation for Simulation-Based Inference. CoRR Yang Yang, Severi Rissanen, Paul E. Chang, Nasrulloh R. B. S. Loka, Daolang Huang, Arno Solin, Markus Heinonen, Luigi Acerbi
2025	A*	`conf`	Progressive Tempering Sampler with Diffusion. ICML Severi Rissanen, Ruikang Ouyang, Jiajun He, Wenlin Chen, Markus Heinonen, Arno Solin, José Miguel Hernández-Lobato
2025	J	`jnl`	Progressive Tempering Sampler with Diffusion. CoRR Severi Rissanen, Ruikang Ouyang, Jiajun He, Wenlin Chen, Markus Heinonen, Arno Solin, José Miguel Hernández-Lobato
2025	J	`jnl`	Repulsive Ensembles for Bayesian Inference in Physics-informed Neural Networks. CoRR Philipp Pilar, Markus Heinonen, Niklas Wahlström
2025	A	`conf`	Robust Classification by Coupling Data Mollification with Label Smoothing. AISTATS Markus Heinonen, Ba-Hien Tran, Michael Kampffmeyer, Maurizio Filippone
2025	J	`jnl`	Scaling Laws for Uncertainty in Deep Learning. CoRR Mattia Rosso, Simone Rossi, Giulio Franzese, Markus Heinonen, Maurizio Filippone
2025	J	`jnl`	Spacetime Geometry of Denoising in Diffusion Models. CoRR Rafal Karczewski, Markus Heinonen, Alison Pouplin, Søren Hauberg, Vikas Garg
2025	J	`jnl`	VitroBert: modeling DILI by pretraining BERT on in vitro data. J. Cheminformatics Muhammad Arslan Masood, Anamya Ajjolli Nagaraja, Katia Belaid, Natalie Mesens, Hugo Ceulemans, Samuel Kaski, Dorota Herman, Markus Heinonen
2025	A	`conf`	What Ails Generative Structure-based Drug Design: Expressivity is Too Little or Too Much? AISTATS Rafal Karczewski, Samuel Kaski, Markus Heinonen, Vikas K. Garg
2024	J	`jnl`	Alignment is Key for Applying Diffusion Models to Retrosynthesis. CoRR Najwa Laabid, Severi Rissanen, Markus Heinonen, Arno Solin, Vikas Garg
2024	—	`conf`	Balancing Imbalanced Toxicity Models: Using MolBERT with Focal Loss. AIDD@ICANN Muhammad Arslan Masood, Samuel Kaski, Hugo Ceulemans, Dorota Herman, Markus Heinonen
2024	A*	`conf`	ClimODE: Climate and Weather Forecasting with Physics-informed Neural ODEs. ICLR Yogesh Verma, Markus Heinonen, Vikas Garg
2024	J	`jnl`	ClimODE: Climate and Weather Forecasting with Physics-informed Neural ODEs. CoRR Yogesh Verma, Markus Heinonen, Vikas Garg
2024	J	`jnl`	Diffusion Models as Cartoonists! The Curious Case of High Density Regions. CoRR Rafal Karczewski, Markus Heinonen, Vikas Garg
2024	J	`jnl`	Field-based Molecule Generation. CoRR Alexandru Dumitrescu, Dani Korpela, Markus Heinonen, Yogesh Verma, Valerii Iakovlev, Vikas Garg, Harri Lähdesmäki
2024	J	`jnl`	Free Hunch: Denoiser Covariance Estimation for Diffusion Models Without Extra Costs. CoRR Severi Rissanen, Markus Heinonen, Arno Solin
2024	J	`jnl`	Human-in-the-loop active learning for goal-oriented molecule generation. J. Cheminformatics Yasmine Nahal, Janosch Menke, Julien Martinelli, Markus Heinonen, Mikhail Kabeshov, Jon Paul Janet, Eva Nittinger, Ola Engkvist, Samuel Kaski
2024	J	`jnl`	Improving Discrete Diffusion Models via Structured Preferential Generation. CoRR Severi Rissanen, Markus Heinonen, Arno Solin
2024	A*	`conf`	Improving robustness to corruptions with multiplicative weight perturbations. NeurIPS Trung Q. Trinh, Markus Heinonen, Luigi Acerbi, Samuel Kaski
2024	J	`jnl`	Improving robustness to corruptions with multiplicative weight perturbations. CoRR Trung Trinh, Markus Heinonen, Luigi Acerbi, Samuel Kaski
2024	A*	`conf`	Input-gradient space particle inference for neural network ensembles. ICLR Trung Q. Trinh, Markus Heinonen, Luigi Acerbi, Samuel Kaski
2024	J	`jnl`	Robust Classification by Coupling Data Mollification with Label Smoothing. CoRR Markus Heinonen, Ba-Hien Tran, Michael Kampffmeyer, Maurizio Filippone
2024	—	`conf`	Towards Interpretable Models of Chemist Preferences for Human-in-the-Loop Assisted Drug Discovery. AIDD@ICANN Yasmine Nahal, Markus Heinonen, Mikhail Kabeshov, Jon Paul Janet, Eva Nittinger, Ola Engkvist, Samuel Kaski
2024	J	`jnl`	What Ails Generative Structure-based Drug Design: Too Little or Too Much Expressivity? CoRR Rafal Karczewski, Samuel Kaski, Markus Heinonen, Vikas Garg
2023	J	`jnl`	AbODE: Ab Initio Antibody Design using Conjoined ODEs. CoRR Yogesh Verma, Markus Heinonen, Vikas Garg
2023	A*	`conf`	AbODE: Ab initio antibody design using conjoined ODEs. ICML Yogesh Verma, Markus Heinonen, Vikas Garg
2023	J	`jnl`	Beyond invariant representation learning: linearly alignable latent spaces for efficient closed-form domain adaptation. CoRR Oliver Struckmeier, Ievgen Redko, Anton Mallasto, Karol Arndt, Markus Heinonen, Ville Kyrki
2023	A*	`conf`	Continuous-Time Functional Diffusion Processes. NeurIPS Giulio Franzese, Giulio Corallo, Simone Rossi, Markus Heinonen, Maurizio Filippone, Pietro Michiardi
2023	J	`jnl`	Continuous-Time Functional Diffusion Processes. CoRR Giulio Franzese, Simone Rossi, Dario Rossi, Markus Heinonen, Maurizio Filippone, Pietro Michiardi
2023	A*	`conf`	Generative Modelling with Inverse Heat Dissipation. ICLR Severi Rissanen, Markus Heinonen, Arno Solin
2023	A	`conf`	Incorporating functional summary information in Bayesian neural networks using a Dirichlet process likelihood approach. AISTATS Vishnu Raj, Tianyu Cui, Markus Heinonen, Pekka Marttinen
2023	J	`jnl`	Input gradient diversity for neural network ensembles. CoRR Trung Q. Trinh, Markus Heinonen, Luigi Acerbi, Samuel Kaski
2023	A*	`conf`	Latent Neural ODEs with Sparse Bayesian Multiple Shooting. ICLR Valerii Iakovlev, Çagatay Yildiz, Markus Heinonen, Harri Lähdesmäki
2023	J	`jnl`	Learning Energy Conserving Dynamics Efficiently with Hamiltonian Gaussian Processes. CoRR Magnus Ross, Markus Heinonen
2023	J	`jnl`	Learning Energy Conserving Dynamics Efficiently with Hamiltonian Gaussian Processes. Trans. Mach. Learn. Res. Magnus Ross, Markus Heinonen
2023	A*	`conf`	Learning Space-Time Continuous Latent Neural PDEs from Partially Observed States. NeurIPS Valerii Iakovlev, Markus Heinonen, Harri Lähdesmäki
2023	J	`jnl`	Learning Space-Time Continuous Neural PDEs from Partially Observed States. CoRR Valerii Iakovlev, Markus Heinonen, Harri Lähdesmäki
2023	J	`jnl`	Learning representations that are closed-form Monge mapping optimal with application to domain adaptation. Trans. Mach. Learn. Res. Oliver Struckmeier, Ievgen Redko, Anton Mallasto, Karol Arndt, Markus Heinonen, Ville Kyrki
2023	J	`jnl`	TCRconv: predicting recognition between T cell receptors and epitopes using contextualized motifs. Bioinform. Emmi Jokinen, Alexandru Dumitrescu, Jani Huuhtanen, Vladimir Gligorijevic, Satu Mustjoki, Richard Bonneau, Markus Heinonen, Harri Lähdesmäki
2023	—	`conf`	Uncertainty-Aware Natural Language Inference with Stochastic Weight Averaging. NoDaLiDa Aarne Talman, Hande Çelikkanat, Sami Virpioja, Markus Heinonen, Jörg Tiedemann
2023	J	`jnl`	Uncertainty-Aware Natural Language Inference with Stochastic Weight Averaging. CoRR Aarne Talman, Hande Çelikkanat, Sami Virpioja, Markus Heinonen, Jörg Tiedemann
2023	J	`jnl`	Understanding deep neural networks through the lens of their non-linearity. CoRR Quentin Bouniot, Ievgen Redko, Anton Mallasto, Charlotte Laclau, Karol Arndt, Oliver Struckmeier, Markus Heinonen, Ville Kyrki, Samuel Kaski
2022	J	`jnl`	Differential Equations and Continuous-Time Deep Learning (Dagstuhl Seminar 22332). Dagstuhl Reports David Duvenaud, Markus Heinonen, Michael Tiemann, Max Welling
2022	J	`jnl`	Generative Modelling With Inverse Heat Dissipation. CoRR Severi Rissanen, Markus Heinonen, Arno Solin
2022	J	`jnl`	Human-in-the-loop assisted de novo molecular design. J. Cheminformatics Iiris Sundin, Alexey Voronov, Haoping Xiao, Kostas Papadopoulos, Esben Jannik Bjerrum, Markus Heinonen, Atanas Patronov, Samuel Kaski, Ola Engkvist
2022	J	`jnl`	Latent Neural ODEs with Sparse Bayesian Multiple Shooting. CoRR Valerii Iakovlev, Çagatay Yildiz, Markus Heinonen, Harri Lähdesmäki
2022	J	`jnl`	Likelihood-free inference with deep Gaussian processes. Comput. Stat. Data Anal. Alexander Aushev, Henri Pesonen, Markus Heinonen, Jukka Corander, Samuel Kaski
2022	J	`jnl`	Look beyond labels: Incorporating functional summary information in Bayesian neural networks. CoRR Vishnu Raj, Tianyu Cui, Markus Heinonen, Pekka Marttinen
2022	J	`jnl`	Modeling binding specificities of transcription factor pairs with random forests. BMC Bioinform. Anni A. Antikainen, Markus Heinonen, Harri Lähdesmäki
2022	A*	`conf`	Modular Flows: Differential Molecular Generation. NeurIPS Yogesh Verma, Samuel Kaski, Markus Heinonen, Vikas Garg
2022	J	`jnl`	Modular Flows: Differential Molecular Generation. CoRR Yogesh Verma, Samuel Kaski, Markus Heinonen, Vikas Garg
2022	A*	`conf`	Tackling covariate shift with node-based Bayesian neural networks. ICML Trung Q. Trinh, Markus Heinonen, Luigi Acerbi, Samuel Kaski
2022	J	`jnl`	Tackling covariate shift with node-based Bayesian neural networks. CoRR Trung Q. Trinh, Markus Heinonen, Luigi Acerbi, Samuel Kaski
2022	A	`conf`	Variational multiple shooting for Bayesian ODEs with Gaussian processes. UAI Pashupati Hegde, Çagatay Yildiz, Harri Lähdesmäki, Samuel Kaski, Markus Heinonen
2021	J	`jnl`	Affine Transport for Sim-to-Real Domain Adaptation. CoRR Anton Mallasto, Karol Arndt, Markus Heinonen, Samuel Kaski, Ville Kyrki
2021	C	`conf`	Bayesian Inference for Optimal Transport with Stochastic Cost. ACML Anton Mallasto, Markus Heinonen, Samuel Kaski
2021	J	`jnl`	Bayesian inference of ODEs with Gaussian processes. CoRR Pashupati Hegde, Çagatay Yildiz, Harri Lähdesmäki, Samuel Kaski, Markus Heinonen
2021	J	`jnl`	Continuous-Time Model-Based Reinforcement Learning. CoRR Çagatay Yildiz, Markus Heinonen, Harri Lähdesmäki
2021	A*	`conf`	Continuous-time Model-based Reinforcement Learning. ICML Çagatay Yildiz, Markus Heinonen, Harri Lähdesmäki
2021	A*	`conf`	De-randomizing MCMC dynamics with the diffusion Stein operator. NeurIPS Zheyang Shen, Markus Heinonen, Samuel Kaski
2021	J	`jnl`	De-randomizing MCMC dynamics with the diffusion Stein operator. CoRR Zheyang Shen, Markus Heinonen, Samuel Kaski
2021	J	`jnl`	Evolving-Graph Gaussian Processes. CoRR David Blanco Mulero, Markus Heinonen, Ville Kyrki
2021	A*	`conf`	Learning continuous-time PDEs from sparse data with graph neural networks. ICLR Valerii Iakovlev, Markus Heinonen, Harri Lähdesmäki
2021	J	`jnl`	Predicting recognition between T cell receptors and epitopes with TCRGP. PLoS Comput. Biol. Emmi Jokinen, Jani Huuhtanen, Satu Mustjoki, Markus Heinonen, Harri Lähdesmäki
2021	A	`conf`	Sparse Gaussian Processes Revisited: Bayesian Approaches to Inducing-Variable Approximations. AISTATS Simone Rossi, Markus Heinonen, Edwin V. Bonilla, Zheyang Shen, Maurizio Filippone
2020	J	`jnl`	Bayesian Inference for Optimal Transport with Stochastic Cost. CoRR Anton Mallasto, Markus Heinonen, Samuel Kaski
2020	J	`jnl`	Learning continuous-time PDEs from sparse data with graph neural networks. CoRR Valerii Iakovlev, Markus Heinonen, Harri Lähdesmäki
2020	A	`conf`	Learning spectrograms with convolutional spectral kernels. AISTATS Zheyang Shen, Markus Heinonen, Samuel Kaski
2020	J	`jnl`	Likelihood-Free Inference with Deep Gaussian Processes. CoRR Alexander Aushev, Henri Pesonen, Markus Heinonen, Jukka Corander, Samuel Kaski
2020	J	`jnl`	Rethinking Sparse Gaussian Processes: Bayesian Approaches to Inducing-Variable Approximations. CoRR Simone Rossi, Markus Heinonen, Edwin V. Bonilla, Zheyang Shen, Maurizio Filippone
2020	J	`jnl`	Sample-efficient reinforcement learning using deep Gaussian processes. CoRR Charles W. L. Gadd, Markus Heinonen, Harri Lähdesmäki, Samuel Kaski
2020	J	`jnl`	Scalable Bayesian neural networks by layer-wise input augmentation. CoRR Trung Q. Trinh, Samuel Kaski, Markus Heinonen
2019	J	`jnl`	Bayesian metabolic flux analysis reveals intracellular flux couplings. Bioinform. Markus Heinonen, Maria Osmala, Henrik Mannerström, Janne Wallenius, Samuel Kaski, Juho Rousu, Harri Lähdesmäki
2019	—	`conf`	Deep Convolutional Gaussian Processes. ECML/PKDD (2) Kenneth Blomqvist, Samuel Kaski, Markus Heinonen
2019	A	`conf`	Deep learning with differential Gaussian process flows. AISTATS Pashupati Hegde, Markus Heinonen, Harri Lähdesmäki, Samuel Kaski
2019	A	`conf`	Harmonizable mixture kernels with variational Fourier features. AISTATS Zheyang Shen, Markus Heinonen, Samuel Kaski
2019	J	`jnl`	Learning spectrograms with convolutional spectral kernels. CoRR Zheyang Shen, Markus Heinonen, Samuel Kaski
2019	J	`jnl`	ODE$^2$VAE: Deep generative second order ODEs with Bayesian neural networks. CoRR Çagatay Yildiz, Markus Heinonen, Harri Lähdesmäki
2019	A*	`conf`	ODE2VAE: Deep generative second order ODEs with Bayesian neural networks. NeurIPS Çagatay Yildiz, Markus Heinonen, Harri Lähdesmäki
2018	J	`jnl`	Bayesian Metabolic Flux Analysis reveals intracellular flux couplings. CoRR Markus Heinonen, Maria Osmala, Henrik Mannerström, Janne Wallenius, Samuel Kaski, Juho Rousu, Harri Lähdesmäki
2018	J	`jnl`	Deep convolutional Gaussian processes. CoRR Kenneth Blomqvist, Samuel Kaski, Markus Heinonen
2018	J	`jnl`	Deep learning with differential Gaussian process flows. CoRR Pashupati Hegde, Markus Heinonen, Harri Lähdesmäki, Samuel Kaski
2018	J	`jnl`	Harmonizable mixture kernels with variational Fourier features. CoRR Zheyang Shen, Markus Heinonen, Samuel Kaski
2018	J	`jnl`	Learning Stochastic Differential Equations With Gaussian Processes Without Gradient Matching. CoRR Çagatay Yildiz, Markus Heinonen, Jukka Intosalmi, Henrik Mannerström, Harri Lähdesmäki
2018	—	`conf`	Learning stochastic differential equations with Gaussian Processes without Gradient Matching. MLSP Çagatay Yildiz, Markus Heinonen, Jukka Intosalmi, Henrik Mannerström, Harri Lähdesmäki
2018	A*	`conf`	Learning unknown ODE models with Gaussian processes. ICML Markus Heinonen, Çagatay Yildiz, Henrik Mannerström, Jukka Intosalmi, Harri Lähdesmäki
2018	J	`jnl`	Learning with multiple pairwise kernels for drug bioactivity prediction. Bioinform. Anna Cichonska, Tapio Pahikkala, Sándor Szedmák, Heli Julkunen, Antti Airola, Markus Heinonen, Tero Aittokallio, Juho Rousu
2018	J	`jnl`	Neural Non-Stationary Spectral Kernel. CoRR Sami Remes, Markus Heinonen, Samuel Kaski
2018	A	`conf`	Variational zero-inflated Gaussian processes with sparse kernels. UAI Pashupati Hegde, Markus Heinonen, Samuel Kaski
2018	J	`jnl`	mGPfusion: predicting protein stability changes with Gaussian process kernel learning and data fusion. Bioinform. Emmi Jokinen, Markus Heinonen, Harri Lähdesmäki
2017	C	`conf`	A Mutually-Dependent Hadamard Kernel for Modelling Latent Variable Couplings. ACML Sami Remes, Markus Heinonen, Samuel Kaski
2017	—	`conf`	Non-Stationary Spectral Kernels. NIPS Sami Remes, Markus Heinonen, Samuel Kaski
2017	J	`jnl`	Non-Stationary Spectral Kernels. CoRR Sami Remes, Markus Heinonen, Samuel Kaski
2016	A	`conf`	Non-Stationary Gaussian Process Regression with Hamiltonian Monte Carlo. AISTATS Markus Heinonen, Henrik Mannerström, Juho Rousu, Samuel Kaski, Harri Lähdesmäki
2016	C	`conf`	Random Fourier Features For Operator-Valued Kernels. ACML Romain Brault, Markus Heinonen, Florence d'Alché-Buc
2016	J	`jnl`	Random Fourier Features for Operator-Valued Kernels. CoRR Romain Brault, Florence d'Alché-Buc, Markus Heinonen
2015	J	`jnl`	Detecting time periods of differential gene expression using Gaussian processes: an application to endothelial cells exposed to radiotherapy dose fraction. Bioinform. Markus Heinonen, Olivier Guipaud, Fabien Milliat, Valérie Buard, Béatrice Micheau, Georges Tarlet, Marc Benderitter, Farida Zehraoui, Florence d'Alché-Buc
2014	J	`jnl`	Learning nonparametric differential equations with operator-valued kernels and gradient matching. CoRR Markus Heinonen, Florence d'Alché-Buc
2012	—	`—`	Computational methods for small molecules. Markus Heinonen
2012	—	`conf`	Efficient Path Kernels for Reaction Function Prediction. BIOINFORMATICS Markus Heinonen, Niko Välimäki, Veli Mäkinen, Juho Rousu
2012	J	`jnl`	Metabolite identification and molecular fingerprint prediction through machine learning. Bioinform. Markus Heinonen, Huibin Shen, Nicola Zamboni, Juho Rousu
2011	J	`jnl`	Computing Atom Mappings for Biochemical Reactions without Subgraph Isomorphism. J. Comput. Biol. Markus Heinonen, Sampsa Lappalainen, Taneli Mielikäinen, Juho Rousu
2010	—	`conf`	Structured Output Prediction of Anti-cancer Drug Activity. PRIB Hongyu Su, Markus Heinonen, Juho Rousu
2006	—	`conf`	Ab Initio Prediction of Molecular Fragments from Tandem Mass Spectrometry Data. German Conference on Bioinformatics Markus Heinonen, Ari Rantanen, Taneli Mielikäinen, Esa Pitkänen, Juha Kokkonen, Juho Rousu