Supervised dimension reduction (SDR) has been a topic of growing interes...
Inverse reinforcement learning (IRL) is a powerful framework to infer an...
The brain structural connectome is generated by a collection of white ma...
Modern datasets witness high-dimensionality and nontrivial geometries of...
Gaussian processes (GPs) are pervasive in functional data analysis, mach...
Over a complete Riemannian manifold of finite dimension, Greene and Wu
i...
Gaussian processes are widely employed as versatile modeling and predict...
High-throughput RNA-sequencing (RNA-seq) technologies are powerful tools...
Dimension reduction is useful for exploratory data analysis. In many
app...
In many applications, data and/or parameters are supported on non-Euclid...
Even with the rise in popularity of over-parameterized models, simple
di...
Current tools for multivariate density estimation struggle when the dens...
Many statistical and machine learning approaches rely on pairwise distan...
There is an immense literature focused on estimating the curvature of an...
Classifiers label data as belonging to one of a set of groups based on i...
Data lying in a high-dimensional ambient space are commonly thought to h...