Search Results for author:
Found 12 papers, 6 papers with code
Learning Latent Dynamics via Invariant Decomposition and (Spatio-)Temporal Transformers
We focus on the setting in which data are available from multiple different instances of a system whose underlying dynamical model is entirely unknown at the outset.
Deep Learning of Causal Structures in High Dimensions
Recent years have seen rapid progress at the intersection between causality and machine learning.
High-Dimensional Undirected Graphical Models for Arbitrary Mixed Data
Methods for learning such graphical models are well developed in the case where all variables are either continuous or discrete, including in high-dimensions.
Scalable Regularised Joint Mixture Models
Building on developments at the intersection of unsupervised learning and regularised regression, we propose an approach for heterogeneous data that allows joint learning of (i) explicit multivariate feature distributions, (ii) high-dimensional regression models and (iii) latent group labels, with both (i) and (ii) specific to latent groups and both elements informing (iii).
On unsupervised projections and second order signals
Motivated by such applications, in this paper we ask whether linear projections can preserve differences in second order structure between latent groups.
The Alzheimer's Disease Prediction Of Longitudinal Evolution (TADPOLE) Challenge: Results after 1 Year Follow-up
TADPOLE's unique results suggest that current prediction algorithms provide sufficient accuracy to exploit biomarkers related to clinical diagnosis and ventricle volume, for cohort refinement in clinical trials for Alzheimer's disease.
Ancestral causal learning in high dimensions with a human genome-wide application
We focus on the setting in which some causal (ancestral) relationships are known (via background knowledge or experimental data) and put forward a general approach that scales to large problems.
Model-based clustering in very high dimensions via adaptive projections
In some examples, MCAP performs well even when the mean signal is entirely removed, leaving differential covariance structure in the high-dimensional space as the only signal.
High-dimensional regression in practice: an empirical study of finite-sample prediction, variable selection and ranking
Our empirical results complement existing theory and provide a resource to compare methods across a range of scenarios and metrics.
Causal Learning via Manifold Regularization
This paper frames causal structure estimation as a machine learning task.
High-dimensional regression over disease subgroups
We consider high-dimensional regression over subgroups of observations.
Exact Estimation of Multiple Directed Acyclic Graphs
This paper considers the problem of estimating the structure of multiple related directed acyclic graph (DAG) models.
