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Binh Vu

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2 papers

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NeurIPS Conference 2025 Conference Paper

Autoencoding Random Forests

Binh Vu
Jan Kapar
Marvin Wright
David Watson

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 low-dimensional embedding of the model that optimally represents relationships in the data. We provide exact and approximate solutions to the decoding problem via constrained optimization, split relabeling, and nearest neighbors regression. These methods effectively invert the compression pipeline, establishing a map from the embedding space back to the input space using splits learned by the ensemble's constituent trees. The resulting decoders are universally consistent under common regularity assumptions. The procedure works with supervised or unsupervised models, providing a window into conditional or joint distributions. We demonstrate various applications of this autoencoder, including powerful new tools for visualization, compression, clustering, and denoising. Experiments illustrate the ease and utility of our method in a wide range of settings, including tabular, image, and genomic data.

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IJCAI Conference 2021 Conference Paper

SPADE: A Semi-supervised Probabilistic Approach for Detecting Errors in Tables

Minh Pham
Craig A. Knoblock
Muhao Chen
Binh Vu
Jay Pujara

Error detection is one of the most important steps in data cleaning and usually requires extensive human interaction to ensure quality. Existing supervised methods in error detection require a significant amount of training data while unsupervised methods rely on fixed inductive biases, which are usually hard to generalize, to solve the problem. In this paper, we present SPADE, a novel semi-supervised probabilistic approach for error detection. SPADE introduces a novel probabilistic active learning model, where the system suggests examples to be labeled based on the agreements between user labels and indicative signals, which are designed to capture potential errors. SPADE uses a two-phase data augmentation process to enrich a dataset before training a deep learning classifier to detect unlabeled errors. In our evaluation, SPADE achieves an average F1-score of 0. 91 over five datasets and yields a 10% improvement compared with the state-of-the-art systems.

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