3 papers
cs.LG2026
Path-independent Flow Matching for Multi-parameter Generative Dynamics
Francisco Téllez, AmirHossein Zamani, Philippe Martin +5
Flow Matching is a powerful framework for learning transport maps between probability distributions. Yet its standard single-parameter formulation is not designed to capture multi-…
cs.LG2025
Random Forest Autoencoders for Guided Representation Learning
Adrien Aumon, Shuang Ni, Myriam Lizotte +3
Extensive research has produced robust methods for unsupervised data visualization. Yet supervised visualization$\unicode{x2013}$where expert labels guide representations$\unicode{…
cs.LG2024
Enhancing Supervised Visualization through Autoencoder and Random Forest Proximities for Out-of-Sample Extension
Shuang Ni, Adrien Aumon, Guy Wolf +2
The value of supervised dimensionality reduction lies in its ability to uncover meaningful connections between data features and labels. Common dimensionality reduction methods emb…