Particle Swarm Optimization Visualization

Implementation MWPCR with R

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

A q-quantile estimator for high-dimensional distributions

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

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

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

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

Video Input Generative Adversarial Imitation Learning

Biomarker selection in penalized regression models

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

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.

The Artificial Bee Colony (ABC) algorithm that optimizes high dimensional continuous model parameters.

Random Forest Two Sample Testing

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 Bayesian multiscale deep learning framework for flows in random media

Statistical inference in sparse high-dimensional additive models

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 …

Codes for Chandra, et al. (2021+). Escaping the curse of dimensionality in Bayesian model based clustering. Please refer to the original paper for details https://arxiv.org/abs/2006.02700

Controlled Invariant Sets in Two Moves