99 citations · 250 across the 23 of their papers we have counts for
24 papers
Flexible Flows for Biological Sequence Design
Yogesh Verma, Dani Korpela, Harri Lähdesmäki +1
Designing functional biological sequences requires navigating vast discrete spaces under strict evolutionary and biophysical constraints. Discrete Flow Matching (DFM) offers a gene…
In-Context Learning for Latent Space Bayesian Optimization
Tuan A. Vu, Harri Lähdesmäki, Julien Martinelli
Bayesian optimization (BO) is a central tool for sample-efficient design, and latent-space Bayesian optimization (LSBO) extends it to structured objects such as molecules and prote…
Modeling Temporal scRNA-seq Data with Latent Gaussian Process and Optimal Transport
Mehmet Yigit Balik, Harri Lähdesmäki
Single-cell RNA sequencing provides insights into gene expression at single-cell resolution, yet inferring temporal processes from these static snapshot measurements remains a fund…
Bayesian Nonparametric Mixed-Effect ODEs with Gaussian Processes
Julien Martinelli, Maksim Sinelnikov, Harri Lähdesmäki +2
Dynamical modelling in pharmacometrics, systems biology, and epidemiology often involves subjects with related but distinct dynamics, observed through temporally sparse and irregul…
Learning Spatiotemporal Dynamical Systems from Point Process Observations
Valerii Iakovlev, Harri Lähdesmäki
Spatiotemporal dynamics models are fundamental for various domains, from heat propagation in materials to oceanic and atmospheric flows. However, currently available neural network…
E(3)-equivariant models cannot learn chirality: Field-based molecular generation
Alexandru Dumitrescu, Dani Korpela, Markus Heinonen +4
Obtaining the desired effect of drugs is highly dependent on their molecular geometries. Thus, the current prevailing paradigm focuses on 3D point-cloud atom representations, utili…
Latent variable model for high-dimensional point process with structured missingness
Maksim Sinelnikov, Manuel Haussmann, Harri Lähdesmäki
Longitudinal data are important in numerous fields, such as healthcare, sociology and seismology, but real-world datasets present notable challenges for practitioners because they…
Estimating treatment effects from single-arm trials via latent-variable modeling
Manuel Haussmann, Tran Minh Son Le, Viivi Halla-aho +5
Randomized controlled trials (RCTs) are the accepted standard for treatment effect estimation but they can be infeasible due to ethical reasons and prohibitive costs. Single-arm tr…
Learning Space-Time Continuous Neural PDEs from Partially Observed States
Valerii Iakovlev, Markus Heinonen, Harri Lähdesmäki
We introduce a novel grid-independent model for learning partial differential equations (PDEs) from noisy and partial observations on irregular spatiotemporal grids. We propose a s…
Latent Neural ODEs with Sparse Bayesian Multiple Shooting
Valerii Iakovlev, Cagatay Yildiz, Markus Heinonen +1
Training dynamic models, such as neural ODEs, on long trajectories is a hard problem that requires using various tricks, such as trajectory splitting, to make model training work i…
A Variational Autoencoder for Heterogeneous Temporal and Longitudinal Data
Mine Öğretir, Siddharth Ramchandran, Dimitrios Papatheodorou +1
The variational autoencoder (VAE) is a popular deep latent variable model used to analyse high-dimensional datasets by learning a low-dimensional latent representation of the data.…
Learning Conditional Variational Autoencoders with Missing Covariates
Siddharth Ramchandran, Gleb Tikhonov, Otto Lönnroth +2
Conditional variational autoencoders (CVAEs) are versatile deep generative models that extend the standard VAE framework by conditioning the generative model with auxiliary covaria…
Scalable mixed-domain Gaussian process modeling and model reduction for longitudinal data
Juho Timonen, Harri Lähdesmäki
Gaussian process (GP) models that combine both categorical and continuous input variables have found use in analysis of longitudinal data and computer experiments. However, standar…
Variational multiple shooting for Bayesian ODEs with Gaussian processes
Pashupati Hegde, Çağatay Yıldız, Harri Lähdesmäki +2
Recent machine learning advances have proposed black-box estimation of unknown continuous-time system dynamics directly from data. However, earlier works are based on approximative…
Continuous-Time Model-Based Reinforcement Learning
Çağatay Yıldız, Markus Heinonen, Harri Lähdesmäki
Model-based reinforcement learning (MBRL) approaches rely on discrete-time state transition models whereas physical systems and the vast majority of control tasks operate in contin…
Sample-efficient reinforcement learning using deep Gaussian processes
Charles Gadd, Markus Heinonen, Harri Lähdesmäki +1
Reinforcement learning provides a framework for learning to control which actions to take towards completing a task through trial-and-error. In many applications observing interact…
Learning continuous-time PDEs from sparse data with graph neural networks
Valerii Iakovlev, Markus Heinonen, Harri Lähdesmäki
The behavior of many dynamical systems follow complex, yet still unknown partial differential equations (PDEs). While several machine learning methods have been proposed to learn P…
ODEVAE: Deep generative second order ODEs with Bayesian neural networks
Çağatay Yıldız, Markus Heinonen, Harri Lähdesmäki
We present Ordinary Differential Equation Variational Auto-Encoder (ODEVAE), a latent second order ODE model for high-dimensional sequential data. Leveraging the advances in de…
Deep learning with differential Gaussian process flows
Pashupati Hegde, Markus Heinonen, Harri Lähdesmäki +1
We propose a novel deep learning paradigm of differential flows that learn a stochastic differential equation transformations of inputs prior to a standard classification or regres…
Learning Stochastic Differential Equations With Gaussian Processes Without Gradient Matching
Cagatay Yildiz, Markus Heinonen, Jukka Intosalmi +2
We introduce a novel paradigm for learning non-parametric drift and diffusion functions for stochastic differential equation (SDE). The proposed model learns to simulate path distr…
Bayesian Metabolic Flux Analysis reveals intracellular flux couplings
Markus Heinonen, Maria Osmala, Henrik Mannerström +4
Metabolic flux balance analyses are a standard tool in analysing metabolic reaction rates compatible with measurements, steady-state and the metabolic reaction network stoichiometr…
mGPfusion: Predicting protein stability changes with Gaussian process kernel learning and data fusion
Emmi Jokinen, Markus Heinonen, Harri Lähdesmäki
Proteins are commonly used by biochemical industry for numerous processes. Refining these proteins' properties via mutations causes stability effects as well. Accurate computationa…
Learning unknown ODE models with Gaussian processes
Markus Heinonen, Cagatay Yildiz, Henrik Mannerström +2
In conventional ODE modelling coefficients of an equation driving the system state forward in time are estimated. However, for many complex systems it is practically impossible to…
Non-Stationary Gaussian Process Regression with Hamiltonian Monte Carlo
Markus Heinonen, Henrik Mannerström, Juho Rousu +2
We present a novel approach for fully non-stationary Gaussian process regression (GPR), where all three key parameters -- noise variance, signal variance and lengthscale -- can be…