Search Results for author: Harri Lähdesmäki

Found 22 papers, 15 papers with code

Estimating treatment effects from single-arm trials via latent-variable modeling

1 code implementation6 Nov 2023 Manuel Haussmann, Tran Minh Son Le, Viivi Halla-aho, Samu Kurki, Jussi Leinonen, Miika Koskinen, Samuel Kaski, Harri Lähdesmäki

Compared to previous methods, our results show improved performance both for direct treatment effect estimation as well as for effect estimation via patient matching.

Variational Inference

Learning Space-Time Continuous Neural PDEs from Partially Observed States

1 code implementation9 Jul 2023 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.

Variational Inference

Latent Neural ODEs with Sparse Bayesian Multiple Shooting

1 code implementation7 Oct 2022 Valerii Iakovlev, Cagatay Yildiz, Markus Heinonen, Harri Lähdesmäki

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 in practice.

Variational Inference

A Variational Autoencoder for Heterogeneous Temporal and Longitudinal Data

1 code implementation20 Apr 2022 Mine Öğretir, Siddharth Ramchandran, Dimitrios Papatheodorou, Harri Lähdesmäki

In this work, we propose the heterogeneous longitudinal VAE (HL-VAE) that extends the existing temporal and longitudinal VAEs to heterogeneous data.


Learning Conditional Variational Autoencoders with Missing Covariates

no code implementations2 Mar 2022 Siddharth Ramchandran, Gleb Tikhonov, Otto Lönnroth, Pekka Tiikkainen, Harri Lähdesmäki

Conditional variational autoencoders (CVAEs) are versatile deep generative models that extend the standard VAE framework by conditioning the generative model with auxiliary covariates.

Variational Inference

Scalable mixed-domain Gaussian processes

no code implementations3 Nov 2021 Juho Timonen, Harri Lähdesmäki

Gaussian process (GP) models that combine both categorical and continuous input variables have found use e. g. in longitudinal data analysis and computer experiments.

Gaussian Processes

Enforcing physics-based algebraic constraints for inference of PDE models on unstructured grids

no code implementations29 Sep 2021 Valerii Iakovlev, Markus Heinonen, Harri Lähdesmäki

Data-driven neural network models have recently shown great success in modelling and learning complex PDE systems.

Variational multiple shooting for Bayesian ODEs with Gaussian processes

1 code implementation21 Jun 2021 Pashupati Hegde, Çağatay Yıldız, Harri Lähdesmäki, Samuel Kaski, Markus Heinonen

Recent machine learning advances have proposed black-box estimation of unknown continuous-time system dynamics directly from data.

Bayesian Inference Gaussian Processes +1

Continuous-Time Model-Based Reinforcement Learning

1 code implementation9 Feb 2021 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 continuous-time.

Model-based Reinforcement Learning reinforcement-learning +1

Multinomial Variational Autoencoders can recover Principal Components

no code implementations1 Jan 2021 James Morton, Justin Silverman, Gleb Tikhonov, Harri Lähdesmäki, Rich Bonneau

Covariance estimation on high dimensional data is a central challenge across multiple scientific disciplines.

Longitudinal Variational Autoencoder

no code implementations17 Jun 2020 Siddharth Ramchandran, Gleb Tikhonov, Kalle Kujanpää, Miika Koskinen, Harri Lähdesmäki

Longitudinal datasets measured repeatedly over time from individual subjects, arise in many biomedical, psychological, social, and other studies.

Imputation Time Series Analysis

Learning continuous-time PDEs from sparse data with graph neural networks

1 code implementation ICLR 2021 Valerii Iakovlev, Markus Heinonen, Harri Lähdesmäki

We demonstrate the model's ability to work with unstructured grids, arbitrary time steps, and noisy observations.

Latent Gaussian process with composite likelihoods and numerical quadrature

no code implementations4 Sep 2019 Siddharth Ramchandran, Miika Koskinen, Harri Lähdesmäki

Clinical patient records are an example of high-dimensional data that is typically collected from disparate sources and comprises of multiple likelihoods with noisy as well as missing values.

Clustering Dimensionality Reduction +1

ODE$^2$VAE: Deep generative second order ODEs with Bayesian neural networks

1 code implementation27 May 2019 Çağatay Yıldız, Markus Heinonen, Harri Lähdesmäki

We present Ordinary Differential Equation Variational Auto-Encoder (ODE$^2$VAE), a latent second order ODE model for high-dimensional sequential data.

Imputation motion prediction +3

Deep learning with differential Gaussian process flows

1 code implementation9 Oct 2018 Pashupati Hegde, Markus Heinonen, Harri Lähdesmäki, Samuel Kaski

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 regression function.

Gaussian Processes General Classification +1

Learning Stochastic Differential Equations With Gaussian Processes Without Gradient Matching

1 code implementation16 Jul 2018 Cagatay Yildiz, Markus Heinonen, Jukka Intosalmi, Henrik Mannerström, Harri Lähdesmäki

We introduce a novel paradigm for learning non-parametric drift and diffusion functions for stochastic differential equation (SDE).

Gaussian Processes

Bayesian Metabolic Flux Analysis reveals intracellular flux couplings

1 code implementation18 Apr 2018 Markus Heinonen, Maria Osmala, Henrik Mannerström, Janne Wallenius, Samuel Kaski, Juho Rousu, Harri Lähdesmäki

Flux analysis methods commonly place unrealistic assumptions on fluxes due to the convenience of formulating the problem as a linear programming model, and most methods ignore the notable uncertainty in flux estimates.

mGPfusion: Predicting protein stability changes with Gaussian process kernel learning and data fusion

1 code implementation8 Feb 2018 Emmi Jokinen, Markus Heinonen, Harri Lähdesmäki

We introduce a Bayesian data fusion model that re-calibrates the experimental and in silico data sources and then learns a predictive GP model from the combined data.

Protein Design

Non-Stationary Gaussian Process Regression with Hamiltonian Monte Carlo

1 code implementation18 Aug 2015 Markus Heinonen, Henrik Mannerström, Juho Rousu, Samuel Kaski, Harri Lähdesmäki

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 simultaneously input-dependent.

GPR regression

Cannot find the paper you are looking for? You can Submit a new open access paper.