KFC: A clusterwise supervised learning procedure based on aggregation of distances
Published in Journal of Statistical Computation and Simulation, 2021
Posts by Collection
portfolio
publications
Consensual aggregation on random projected high-dimensional features for regression
Published in HAL, 2022
Machine learning methods applied to the global modeling of event-driven pitch angle diffusion coefficients during high speed streams
Published in Journal of Frontier Physics, 2022
Gradient COBRA: A kernel-based consensual aggregation for regression
Published in Journal of Data Science Statistics and Visualisation (JDSSV), 2023
☛ Download paper. ☛ Read gradientcobra python library here. ☛ Read -codes documentation here).
talks
Consensual Aggregation of Clusters based on Bregman Divergences to Improve Predictive Models
Published:
More information about “8th Meeting of Young Statisticians”.
Introduction of Machine Learning and Some Methods of Supervised Classification
Published:
It was a mission in Cambodia. I gave a short course of introduction to machine learning at Institut de Technologie du Cambodge (ITC).
KFC: A Clusterwise Supervised Learning Procedure based on Aggregation of Distances
Published:
It was an online summer school. The talk was about the applications of KFC procedure on the domain of energy. More information about the summer school can be found here. The video of the talk can be found here.
A kernel-based Consensual Aggregation for Regression
Published:
More information about Journées de Statistique 2021. The talk was about my research topic of a kernel-based aggregation method for regression. The slide can be found here.
Supervised Machine Learning with Some Green Algorithms
Published:
Forum for Pushing the Boundary (FPB) is a group of Cambodian scholars, creating with a purpose of sharing knowledge around Math, Sciences and general topics to Cambodian students. Here, I share one talk with them about ML. The slide can be found (here).
$K$-means Clustering Algorithm via Vector Quantization
Published:
The talk is about a mathematical model of data compression known as Vector Quantization and its algorithmic adaptation, the well-known K-means algorithm. The goal of this talk is to show how the mathematical theories of vector quantization model became the fundamental structure of K-means clustering algorithm. The slide can be found here.
Estimating Balloon-observed Gravity Wave Moment Fluxes using Machine Learning and Input from Reanalysis
Published:
The talk took place at the SCALES conference from 26th to 30th June 2023 in Mainz, Germany. It is about reconstructing GWMFs observed by super-pressure balloons of Stratéole-2 campaign using ML and the description of large-scale flow from ERA5. It is the first part of my Postdoctoral research in atmospheric science. The slide can be found here.
Fundamental and Hands-on Introduction to Machine Learning
Published:
This talk aims at providing key concepts of Machine Learning through real examples. Linear models in regression and classification are introduced along with some tasks. Some gradient-based optimization and penalization techniques are also discussed from Linear Regression to Deep Neural Networks. The slide can be found here: Introduction to Machine Learning. For those who want to play with the codes, you can find jupyter notebook of the simulation here: Teaching repository.