A Flexible and Powerful Parameter Server for large-scale machine learning

Implements "Clustering a Million Faces by Identity"

A numerical library for High-Dimensional option Pricing problems, including Fourier transform methods, Monte Carlo methods and the Deep Galerkin method

Particle Swarm Optimization Visualization

Solution Paths of Sparse Linear Support Vector Machine with Lasso or ELastic-Net Regularization

DataHigh: A graphical user interface for visualizing and interacting with high-dimensional neural activity

Regularization Paths for Huber Loss Regression and Quantile Regression Penalized by Lasso or Elastic-Net

BioMM: Biological-informed Multi-stage Machine learning framework for phenotype prediction using omics data

Controlled Invariant Sets in Two Moves

Video Input Generative Adversarial Imitation Learning

Quantification of local similarity across two maps/embeddings

Random Forest Two Sample Testing

Replicate the results of nowcasting housing sales by Google Queries, using Bayesian Structural Time-Series Model (Choi & Varian, 2009, 2012).

Characterization of intra-individual variability using physiologically relevant measurements provides important insights into fundamental biological questions ranging from cell type identity to tumor development. For each individual, the data measurements can be written as a matrix with the different subsamples of the individual recorded in the columns and the different phenotypic units recorded in the rows. Datasets of this type are called high-dimensional transposable data. The HDTD package provides functions for conducting statistical inference for the mean relationship between the row and column variables and for the covariance structure within and between the row and column variables.

R codes and dataset for the estimation of the high-dimensional state space model proposed in the paper "A dynamic factor model approach to incorporate Big Data in state space models for official statistics" with Franz Palm, Stephan Smeekes and Jan van den Brakel. Working paper version: https://arxiv.org/abs/1901.11355 or https://www.cbs.nl/en-gb/background/2020/08/a-dynamic-factor-model-for-unemployment-statistics.

Biomarker selection in penalized regression models

A fast, accurate, and modularized dimensionality reduction approach based on diffusion harmonics and graph layouts. Escalates to millions of samples on a personal laptop. Adds high-dimensional big data intrinsic structure to your clustering and data visualization workflow.

A q-quantile estimator for high-dimensional distributions

Lossless conversion algorithm for converting Cortical Learning Algorithm binary vectors to Modular Composite Representation vectors. Implements Integer Sparse Distributed Memory.

An R package for regression analysis of data from extreme sampling

Statistical inference in sparse high-dimensional additive models.

Implementation MWPCR with R