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09/01/2021
Information-theoretic Classification Accuracy: A Criterion that Guides Data-driven Combination of Ambiguous Outcome Labels in Multi-class Classification
Outcome labeling ambiguity and subjectivity are ubiquitous in real-world...
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02/28/2021
Adversarial Information Bottleneck
The information bottleneck (IB) principle has been adopted to explain de...
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02/18/2021
A Mathematical Principle of Deep Learning: Learn the Geodesic Curve in the Wasserstein Space
Recent studies revealed the mathematical connection of deep neural netwo...
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10/16/2020
Learnable Graph-regularization for Matrix Decomposition
Low-rank approximation models of data matrices have become important mac...
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05/20/2020
Tessellated Wasserstein Auto-Encoders
Non-adversarial generative models such as variational auto-encoder (VAE)...
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02/10/2020
Distributed Bayesian Matrix Decomposition for Big Data Mining and Clustering
Matrix decomposition is one of the fundamental tools to discover knowled...
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12/05/2019
Towards Understanding Residual and Dilated Dense Neural Networks via Convolutional Sparse Coding
Convolutional neural network (CNN) and its variants have led to many sta...
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11/25/2019
Matrix Normal PCA for Interpretable Dimension Reduction and Graphical Noise Modeling
Principal component analysis (PCA) is one of the most widely used dimens...
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07/28/2018
Group-sparse SVD Models and Their Applications in Biological Data
Sparse Singular Value Decomposition (SVD) models have been proposed for ...
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12/09/2017
Bayesian Joint Matrix Decomposition for Data Integration with Heterogeneous Noise
Matrix decomposition is a popular and fundamental approach in machine le...
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10/13/2017
Sparse Weighted Canonical Correlation Analysis
Given two data matrices X and Y, sparse canonical correlation analysis (...
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07/28/2017
Sparse Deep Nonnegative Matrix Factorization
Nonnegative matrix factorization is a powerful technique to realize dime...
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07/25/2017
A Unified Joint Matrix Factorization Framework for Data Integration
Nonnegative matrix factorization (NMF) is a powerful tool in data explor...
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09/21/2016
Network-regularized Sparse Logistic Regression Models for Clinical Risk Prediction and Biomarker Discovery
Molecular profiling data (e.g., gene expression) has been used for clini...
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03/19/2016
