49 citations · 241 across the 42 of their papers we have counts for
6 papers · 1 filter
Computationally Efficient Approximations for Matrix-based Renyi's Entropy
Tieliang Gong, Yuxin Dong, Shujian Yu +1
The recently developed matrix based Renyi's entropy enables measurement of information in data simply using the eigenspectrum of symmetric positive semi definite (PSD) matrices in…
Gated Information Bottleneck for Generalization in Sequential Environments
Francesco Alesiani, Shujian Yu, Xi Yu
Deep neural networks suffer from poor generalization to unseen environments when the underlying data distribution is different from that in the training set. By learning minimum su…
Information Theoretic Structured Generative Modeling
Bo Hu, Shujian Yu, Jose C. Principe
Rényi's information provides a theoretical foundation for tractable and data-efficient non-parametric density estimation, based on pair-wise evaluations in a reproducing kernel Hil…
Learning to Transfer with von Neumann Conditional Divergence
Ammar Shaker, Shujian Yu, Daniel Oñoro-Rubio
The similarity of feature representations plays a pivotal role in the success of problems related to domain adaptation. Feature similarity includes both the invariance of marginal…
Deep Deterministic Information Bottleneck with Matrix-based Entropy Functional
Xi Yu, Shujian Yu, Jose C. Principe
We introduce the matrix-based Renyi's -order entropy functional to parameterize Tishby et al. information bottleneck (IB) principle with a neural network. We term our methodolog…
Measuring Dependence with Matrix-based Entropy Functional
Shujian Yu, Francesco Alesiani, Xi Yu +2
Measuring the dependence of data plays a central role in statistics and machine learning. In this work, we summarize and generalize the main idea of existing information-theoretic…