Compared to previous methods, our results show improved performance both for direct treatment effect estimation as well as for effect estimation via patient matching.
We introduce a novel grid-independent model for learning partial differential equations (PDEs) from noisy and partial observations on irregular spatiotemporal grids.
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.
In this work, we propose the heterogeneous longitudinal VAE (HL-VAE) that extends the existing temporal and longitudinal VAEs to heterogeneous data.
Conditional variational autoencoders (CVAEs) are versatile deep generative models that extend the standard VAE framework by conditioning the generative model with auxiliary covariates.
Gaussian process (GP) models that combine both categorical and continuous input variables have found use e. g. in longitudinal data analysis and computer experiments.
Data-driven neural network models have recently shown great success in modelling and learning complex PDE systems.
Recent machine learning advances have proposed black-box estimation of unknown continuous-time system dynamics directly from data.
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.
Covariance estimation on high dimensional data is a central challenge across multiple scientific disciplines.
In model-based reinforcement learning efficiency is improved by learning to simulate the world dynamics.
Longitudinal datasets measured repeatedly over time from individual subjects, arise in many biomedical, psychological, social, and other studies.
The lgpr tool is implemented as a comprehensive and user-friendly R-package.
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.
We present Ordinary Differential Equation Variational Auto-Encoder (ODE$^2$VAE), a latent second order ODE model for high-dimensional sequential data.
Ranked #1 on Video Prediction on CMU Mocap-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 regression function.
We introduce a novel paradigm for learning non-parametric drift and diffusion functions for stochastic differential equation (SDE).
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.
In conventional ODE modelling coefficients of an equation driving the system state forward in time are estimated.
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.
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.