4 papers
Minimax Rates and Spectral Distillation for Tree Ensembles
Binh Duc Vu, David S. Watson
Tree ensembles such as random forests (RFs) and gradient boosting machines (GBMs) are among the most widely used supervised learners, yet their theoretical properties remain incomp…
Intelligent Knowledge Mining Framework: Bridging AI Analysis and Trustworthy Preservation
Binh Vu
The unprecedented proliferation of digital data presents significant challenges in access, integration, and value creation across all data-intensive sectors. Valuable information i…
Autoencoding Random Forests
Binh Duc Vu, Jan Kapar, Marvin Wright +1
We propose a principled method for autoencoding with random forests. Our strategy builds on foundational results from nonparametric statistics and spectral graph theory to learn a…
Picasso: Model-free Feature Visualization
Binh Vu, Igor Markov
Today, Machine Learning (ML) applications can have access to tens of thousands of features. With such feature sets, efficiently browsing and curating subsets of most relevant featu…