high-dimensionality

Robust and fast Monte Carlo algorithm for high dimension integration

A new efficient subspace and K-Means clustering based method to improve Collaborative Filtering

[ICML2025] Canonical Rank Adaptation: An Efficient Fine-Tuning Strategy for Vision Transformers

Implementation of the block-descent-CoCoLasso, inspired from the article https://arxiv.org/pdf/1510.07123.pdf

Modelos de alta dimensionalidade para previsão do IPCA

In this project I look at the high dimensional MNIST dataset of handwritten digits and use PCA, t-SNE and Topological data analysis (TDA) to visualise and understand the dataset.

An Efficeint and Fast Wrapper-based High-dimensional Feature Selection(SIFE) in MATLAB

Companion code repository for the textbook Macroeconometrics by Alessia Paccagnini, with Python, R, MATLAB and Dynare examples

This repository contains the R-Package for a novel time series forecasting method designed to handle very large sets of predictive signals, many of which may be irrelevant or have only short-lived predictive power.

CVaR Portfolio Optimization in High Dimensions

Quantum neural network research implementing multi-dimensional neuron representations. Explores theoretical integration of quantum computing principles into neural systems to investigate emergent cognition and consciousness.

https://new.math.uiuc.edu/math595/public/resources/illimac/kaiser/

Efficient Learning of Minimax Risk Classifiers in High Dimensions

CFOF developed in Python. Based on Angiulli's works : https://arxiv.org/pdf/1901.04992v2.pdf

Implementation of Reinforcement Learning algorithms (REINFORCE, Actor-Critic, PPO) applied to the Hopper task. The project explores Uniform Domain Randomization (UDR) to improve generalization and introduces sensitivity analysis using Sobol indices to identify key parameters, optimizing training efficiency and model performance.

A Case study is to classify the genetic mutation classes.

Tracking LLM reasoning through Embedding Space and Graph Structures

Clustered heatmap of plant densities and multiple related variables in gardens

Lightweight HNSW for experimenting with approximate nearest neighbors.

Implementation of Gaussian Process regression / kernel based interpolants with QMC design points and kernels, including experimentation infrastructure.