A Julia package for simulations on random trees.
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Updated
Apr 6, 2020 - Jupyter Notebook
A Julia package for simulations on random trees.
Object Detection using hog descriptor and Random forest
Developed a ML assisted stock trading strategy to long or short a stock by training a random forest learner (random tree with bagging), details see the Final-Project-Report.
A collection of python scripts for generating random dependency trees.
Sorbonne Université Master 2 Cours MU5IN550
Lean 4 formalization of Critical trees are neither too short nor too fat (arXiv:2311.06163v2).
Lean 4 formalization of freezing growth for random recursive trees (arXiv:2505.16571v2), with verified Introduction results.
Lean formalization of seven main theorems of Uniform attachment with freezing; documented scope and remaining obligations.
OpenCV Machine Learning samples
Soccer match predictions using machine learning techniques, including data preprocessing and cleanup, feature extraction and derivation, and model optimization. Done as a final project for CS4840 - Intro to Machine Learning.
Proyectos de Aprendizaje Automático Supervisado
Feature Engineering with binary variables from random trees
Lean 4 formalization of condensation in subcritical Cauchy Bienaymé trees (Kortchemski–Vetter, ECP 2025)
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