ML Anthology

Heinonen, Markus

50 publications

ICML 2025 Devil Is in the Details: Density Guidance for Detail-Aware Generation with Flow Models Rafal Karczewski, Markus Heinonen, Vikas K Garg
ICLR 2025 Diffusion Models as Cartoonists: The Curious Case of High Density Regions Rafal Karczewski, Markus Heinonen, Vikas Garg
ICLR 2025 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 Equivariant Denoisers Cannot Copy Graphs: Align Your Graph Diffusion Models Najwa Laabid, Severi Rissanen, Markus Heinonen, Arno Solin, Vikas Garg
ICLR 2025 Free Hunch: Denoiser Covariance Estimation for Diffusion Models Without Extra Costs Severi Rissanen, Markus Heinonen, Arno Solin
CVPR 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
TMLR 2025 Gaussian Processes with Bayesian Inference of Covariate Couplings Mattia Rosso, Juho Ylä-Jääski, Zheyang Shen, Markus Heinonen, Maurizio Filippone
ICLRW 2025 Multi-Modal Representation Learning for Molecules Muhammad Arslan Masood, Markus Heinonen, Samuel Kaski
ICML 2025 Progressive Tempering Sampler with Diffusion Severi Rissanen, Ruikang Ouyang, Jiajun He, Wenlin Chen, Markus Heinonen, Arno Solin, José Miguel Hernández-Lobato
AISTATS 2025 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
ICMLW 2024 Aligned Diffusion Models for Retrosynthesis Najwa Laabid, Severi Rissanen, Markus Heinonen, Arno Solin, Vikas Garg
ICMLW 2024 Aligned Diffusion Models for Retrosynthesis Najwa Laabid, Severi Rissanen, Markus Heinonen, Arno Solin, Vikas Garg
ICLR 2024 ClimODE: Climate and Weather Forecasting with Physics-Informed Neural ODEs Yogesh Verma, Markus Heinonen, Vikas Garg
ICMLW 2024 Conditional Flow Matching for Time Series Modelling Ella Tamir, Najwa Laabid, Markus Heinonen, Vikas Garg, Arno Solin
ICMLW 2024 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
NeurIPS 2024 Improving Robustness to Corruptions with Multiplicative Weight Perturbations Trung Trinh, Markus Heinonen, Luigi Acerbi, Samuel Kaski
ICLR 2024 Input-Gradient Space Particle Inference for Neural Network Ensembles Trung Trinh, Markus Heinonen, Luigi Acerbi, Samuel Kaski
ICML 2023 AbODE: Ab Initio Antibody Design Using Conjoined ODEs Yogesh Verma, Markus Heinonen, Vikas Garg
ICMLW 2023 AbODE: Ab Initio Antibody Design Using Conjoined ODEs Yogesh Verma, Markus Heinonen, Vikas Garg
ICMLW 2023 AbODE: Ab Initio Antibody Design Using Conjoined ODEs Yogesh Verma, Markus Heinonen, Vikas Garg
NeurIPS 2023 Continuous-Time Functional Diffusion Processes Giulio Franzese, Giulio Corallo, Simone Rossi, Markus Heinonen, Maurizio Filippone, Pietro Michiardi
ICLR 2023 Generative Modelling with Inverse Heat Dissipation Severi Rissanen, Markus Heinonen, Arno Solin
AISTATS 2023 Incorporating Functional Summary Information in Bayesian Neural Networks Using a Dirichlet Process Likelihood Approach Vishnu Raj, Tianyu Cui, Markus Heinonen, Pekka Marttinen
ICLR 2023 Latent Neural ODEs with Sparse Bayesian Multiple Shooting Valerii Iakovlev, Cagatay Yildiz, Markus Heinonen, Harri Lähdesmäki
TMLR 2023 Learning Energy Conserving Dynamics Efficiently with Hamiltonian Gaussian Processes Magnus Ross, Markus Heinonen
TMLR 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
NeurIPS 2023 Learning Space-Time Continuous Latent Neural PDEs from Partially Observed States Valerii Iakovlev, Markus Heinonen, Harri Lähdesmäki
NeurIPS 2022 Modular Flows: Differential Molecular Generation Yogesh Verma, Samuel Kaski, Markus Heinonen, Vikas Garg
NeurIPSW 2022 Modular Flows: Differential Molecular Generation Yogesh Verma, Samuel Kaski, Markus Heinonen, Vikas Garg
NeurIPSW 2022 Modular Flows: Differential Molecular Generation Yogesh Verma, Samuel Kaski, Markus Heinonen, Vikas Garg
ICML 2022 Tackling Covariate Shift with Node-Based Bayesian Neural Networks Trung Q Trinh, Markus Heinonen, Luigi Acerbi, Samuel Kaski
UAI 2022 Variational Multiple Shooting for Bayesian ODEs with Gaussian Processes Pashupati Hegde, Çağatay Yıldız, Harri Lähdesmäki, Samuel Kaski, Markus Heinonen
AISTATS 2021 Sparse Gaussian Processes Revisited: Bayesian Approaches to Inducing-Variable Approximations Simone Rossi, Markus Heinonen, Edwin Bonilla, Zheyang Shen, Maurizio Filippone
ACML 2021 Bayesian Inference for Optimal Transport with Stochastic Cost Anton Mallasto, Markus Heinonen, Samuel Kaski
ICML 2021 Continuous-Time Model-Based Reinforcement Learning Cagatay Yildiz, Markus Heinonen, Harri Lähdesmäki
NeurIPS 2021 De-Randomizing MCMC Dynamics with the Diffusion Stein Operator Zheyang Shen, Markus Heinonen, Samuel Kaski
ICLR 2021 Learning Continuous-Time PDEs from Sparse Data with Graph Neural Networks Valerii Iakovlev, Markus Heinonen, Harri Lähdesmäki
AISTATS 2020 Learning Spectrograms with Convolutional Spectral Kernels Zheyang Shen, Markus Heinonen, Samuel Kaski
NeurIPSW 2020 Likelihood-Free Inference with Deep Gaussian Processes Alexander Aushev, Henri Pesonen, Markus Heinonen, Jukka Corander, Samuel Kaski
ECML-PKDD 2019 Deep Convolutional Gaussian Processes Kenneth Blomqvist, Samuel Kaski, Markus Heinonen
AISTATS 2019 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
NeurIPS 2019 ODE2VAE: Deep Generative Second Order ODEs with Bayesian Neural Networks Cagatay Yildiz, Markus Heinonen, Harri Lahdesmaki
ICML 2018 Learning Unknown ODE Models with Gaussian Processes Markus Heinonen, Cagatay Yildiz, Henrik Mannerström, Jukka Intosalmi, Harri Lähdesmäki
UAI 2018 Variational Zero-Inflated Gaussian Processes with Sparse Kernels Pashupati Hegde, Markus Heinonen, Samuel Kaski
ACML 2017 A Mutually-Dependent Hadamard Kernel for Modelling Latent Variable Couplings Sami Remes, Markus Heinonen, Samuel Kaski
NeurIPS 2017 Non-Stationary Spectral Kernels Sami Remes, Markus Heinonen, Samuel Kaski
AISTATS 2016 Non-Stationary Gaussian Process Regression with Hamiltonian Monte Carlo Markus Heinonen, Henrik Mannerström, Juho Rousu, Samuel Kaski, Harri Lähdesmäki
ACML 2016 Random Fourier Features for Operator-Valued Kernels Romain Brault, Markus Heinonen, Florence Buc