Influenced by the dynamic changes in the severity of illness, patients usually take
examinations in hospitals irregularly, producing a large volume of irregular medical time …

Accurate prediction of mortality risk is important for evaluating early treatments, detecting
high-risk patients and improving healthcare outcomes. Predicting mortality risk from the …

We propose a new method for performing multiscale analysis of functions defined on the
vertices of a finite connected weighted graph. Our approach relies on a random spanning …

Tree-based ensemble models are heavy memory-wise. An undesired state of affairs
considering nowadays datasets, memory-constrained environment and fitting/prediction …

Discrete wavelet methods, originally formulated in the setting of regularly sampled signals,
can be adapted to data defined on a point cloud if some multiresolution structure is imposed …

We propose a probe for the analysis of deep learning architectures that is based on machine
learning and approximation theoretical principles. Given a deep learning architecture and a …

Tree ensembles, such as Random Forest (RF), are popular methods in machine learning
because of their efficiency and superior performance. However, they always grow big trees …

Актуальность и цели. Рассмотрена актуальная проблема построения графов принятия
решений оптимальной структуры, которые используются для решения задач бинарной …

In this paper we introduce a significant improvement to the popular tree-based Stochastic
Gradient Boosting algorithm using a wavelet decomposition of the trees. This approach is …

In this paper we propose a function space approach to Representation Learning and the
analysis of the representation layers in deep learning architectures. We show how to …