The stable under iteration (STIT) tessellation process is a stochastic process that produces a
recursive partition of space with cut directions drawn independently from a distribution over …

The stable under iterated tessellation (STIT) process is a stochastic process that produces a
recursive partition of space with cut directions drawn independently from a distribution over …

This report is concerned with the Mondrian process and its applications in machine learning.
The Mondrian process is a guillotine-partition-valued stochastic process that possesses an …

Introduced by Breiman, Random Forests are widely used classification and regression
algorithms. While being initially designed as batch algorithms, several variants have been …

Natural data observed in $\mathbb {R}^ n $ is often constrained to an $ m $-dimensional
manifold $\mathcal {M} $, where $ m< n $. This work focuses on the task of building …

Probabilistic models of data sets often exhibit salient geometric structure. Such a
phenomenon is summed up in the manifold distribution hypothesis and can be exploited in …

We present a framework for learning probability distributions on topologically non-trivial
manifolds, utilizing normalizing flows. Current methods focus on manifolds that are …

Random forests are a popular class of algorithms used for regression and classification. The
algorithm introduced by Breiman in 2001 and many of its variants are ensembles of …

Abstract Space partitioning methods such as random forests and the Mondrian process are
powerful machine learning methods for multi-dimensional and relational data, and are …

We derive concentration inequalities for the supremum norm of the difference between a
kernel density estimator (KDE) and its point-wise expectation that hold uniformly over the …