Lähdesmäki, Harri

23 publications

ICML 2025 Bayesian Basis Function Approximation for Scalable Gaussian Process Priors in Deep Generative Models Mehmet Yiğit Balık, Maksim Sinelnikov, Priscilla Ong, Harri Lähdesmäki

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 High-Dimensional Bayesian Optimisation with Gaussian Process Prior Variational Autoencoders Siddharth Ramchandran, Manuel Haussmann, Harri Lähdesmäki

TMLR 2025 Latent Mixed-Effect Models for High-Dimensional Longitudinal Data Priscilla Ong, Manuel Haussmann, Otto Lönnroth, Harri Lähdesmäki

ICLR 2025 Learning Spatiotemporal Dynamical Systems from Point Process Observations Valerii Iakovlev, Harri Lähdesmäki

AISTATS 2024 Estimating Treatment Effects from Single-Arm Trials via Latent-Variable Modeling Manuel Haussmann, Tran Minh Son Le, Viivi Halla-aho, Samu Kurki, Jussi Leinonen, Miika Koskinen, Samuel Kaski, Harri Lähdesmäki

ICML 2024 Latent Variable Model for High-Dimensional Point Process with Structured Missingness Maksim Sinelnikov, Manuel Haussmann, Harri Lähdesmäki

ICMLW 2024 Learning High-Dimensional Mixed Models via Amortized Variational Inference Priscilla Ong, Manuel Haussmann, Harri Lähdesmäki

ICMLW 2023 Adverse Event Prediction Using a Task-Specific Generative Model Otto Lönnroth, Siddharth Ramchandran, Pekka Tiikkainen, Mine Öğretir, Jussi Leinonen, Harri Lähdesmäki

ICLR 2023 Latent Neural ODEs with Sparse Bayesian Multiple Shooting Valerii Iakovlev, Cagatay Yildiz, Markus Heinonen, Harri Lähdesmäki

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

ICMLW 2023 Longitudinal Variational Autoencoder for Compositional Data Analysis Mine Öğretir, Harri Lähdesmäki, Jamie Norton

UAI 2022 Variational Multiple Shooting for Bayesian ODEs with Gaussian Processes Pashupati Hegde, Çağatay Yıldız, Harri Lähdesmäki, Samuel Kaski, Markus Heinonen

AISTATS 2021 Latent Gaussian Process with Composite Likelihoods and Numerical Quadrature Siddharth Ramchandran, Miika Koskinen, Harri Lähdesmäki

AISTATS 2021 Longitudinal Variational Autoencoder Siddharth Ramchandran, Gleb Tikhonov, Kalle Kujanpää, Miika Koskinen, Harri Lähdesmäki

ICML 2021 Continuous-Time Model-Based Reinforcement Learning Cagatay Yildiz, Markus Heinonen, Harri Lähdesmäki

ICLR 2021 Learning Continuous-Time PDEs from Sparse Data with Graph Neural Networks Valerii Iakovlev, Markus Heinonen, Harri Lähdesmäki

AISTATS 2019 Deep Learning with Differential Gaussian Process Flows Pashupati Hegde, Markus Heinonen, Harri Lähdesmäki, 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

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

MLJ 2008 Learning the Structure of Dynamic Bayesian Networks from Time Series and Steady State Measurements Harri Lähdesmäki, Ilya Shmulevich

MLJ 2003 On Learning Gene Regulatory Networks Under the Boolean Network Model Harri Lähdesmäki, Ilya Shmulevich, Olli Yli-Harja