49 citations · 241 across the 42 of their papers we have counts for
42 papers
Revisiting the Robustness of the Minimum Error Entropy Criterion: A Transfer Learning Case Study
Luis Pedro Silvestrin, Shujian Yu, Mark Hoogendoorn
Coping with distributional shifts is an important part of transfer learning methods in order to perform well in real-life tasks. However, most of the existing approaches in this ar…
Coping with Change: Learning Invariant and Minimum Sufficient Representations for Fine-Grained Visual Categorization
Shuo Ye, Shujian Yu, Wenjin Hou +2
Fine-grained visual categorization (FGVC) is a challenging task due to similar visual appearances between various species. Previous studies always implicitly assume that the traini…
Higher-order Organization in the Human Brain from Matrix-Based Rényi's Entropy
Qiang Li, Shujian Yu, Kristoffer H Madsen +2
Pairwise metrics are often employed to estimate statistical dependencies between brain regions, however they do not capture higher-order information interactions. It is critical to…
Causal Recurrent Variational Autoencoder for Medical Time Series Generation
Hongming Li, Shujian Yu, Jose Principe
We propose causal recurrent variational autoencoder (CR-VAE), a novel generative model that is able to learn a Granger causal graph from a multivariate time series x and incorporat…
The Conditional Cauchy-Schwarz Divergence with Applications to Time-Series Data and Sequential Decision Making
Shujian Yu, Hongming Li, Sigurd Løkse +2
The Cauchy-Schwarz (CS) divergence was developed by Príncipe et al. in 2000. In this paper, we extend the classic CS divergence to quantify the closeness between two conditional di…
CI-GNN: A Granger Causality-Inspired Graph Neural Network for Interpretable Brain Network-Based Psychiatric Diagnosis
Kaizhong Zheng, Shujian Yu, Badong Chen
There is a recent trend to leverage the power of graph neural networks (GNNs) for brain-network based psychiatric diagnosis, which,in turn, also motivates an urgent need for psychi…
Robust and Fast Measure of Information via Low-rank Representation
Yuxin Dong, Tieliang Gong, Shujian Yu +2
The matrix-based Rényi's entropy allows us to directly quantify information measures from given data, without explicit estimation of the underlying probability distribution. This i…
Functional Connectome of the Human Brain with Total Correlation
Qiang Li, Greg Ver Steeg, Shujian Yu +1
Recent studies proposed the use of Total Correlation to describe functional connectivity among brain regions as a multivariate alternative to conventional pair-wise measures such a…
Information-Theoretic Hashing for Zero-Shot Cross-Modal Retrieval
Yufeng Shi, Shujian Yu, Duanquan Xu +1
Zero-shot cross-modal retrieval (ZS-CMR) deals with the retrieval problem among heterogenous data from unseen classes. Typically, to guarantee generalization, the pre-defined class…
Principle of Relevant Information for Graph Sparsification
Shujian Yu, Francesco Alesiani, Wenzhe Yin +2
Graph sparsification aims to reduce the number of edges of a graph while maintaining its structural properties. In this paper, we propose the first general and effective informatio…
Optimal Randomized Approximations for Matrix based Renyi's Entropy
Yuxin Dong, Tieliang Gong, Shujian Yu +1
The Matrix-based Renyi's entropy enables us to directly measure information quantities from given data without the costly probability density estimation of underlying distributions…
BrainIB: Interpretable Brain Network-based Psychiatric Diagnosis with Graph Information Bottleneck
Kaizhong Zheng, Shujian Yu, Baojuan Li +2
Developing a new diagnostic models based on the underlying biological mechanisms rather than subjective symptoms for psychiatric disorders is an emerging consensus. Recently, machi…
Multi-view Information Bottleneck Without Variational Approximation
Qi Zhang, Shujian Yu, Jingmin Xin +1
By "intelligently" fusing the complementary information across different views, multi-view learning is able to improve the performance of classification tasks. In this work, we ext…
R2-Trans:Fine-Grained Visual Categorization with Redundancy Reduction
Yu Wang, Shuo Ye, Shujian Yu +1
Fine-grained visual categorization (FGVC) aims to discriminate similar subcategories, whose main challenge is the large intraclass diversities and subtle inter-class differences. E…
Deep Deterministic Independent Component Analysis for Hyperspectral Unmixing
Hongming Li, Shujian Yu, Jose C. Principe
We develop a new neural network based independent component analysis (ICA) method by directly minimizing the dependence amongst all extracted components. Using the matrix-based R{é…
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…
Bilevel Continual Learning
Ammar Shaker, Francesco Alesiani, Shujian Yu +1
Continual learning (CL) studies the problem of learning a sequence of tasks, one at a time, such that the learning of each new task does not lead to the deterioration in performanc…
Modular-Relatedness for Continual Learning
Ammar Shaker, Shujian Yu, Francesco Alesiani
In this paper, we propose a continual learning (CL) technique that is beneficial to sequential task learners by improving their retained accuracy and reducing catastrophic forgetti…
Learning an Interpretable Graph Structure in Multi-Task Learning
Shujian Yu, Francesco Alesiani, Ammar Shaker +1
We present a novel methodology to jointly perform multi-task learning and infer intrinsic relationship among tasks by an interpretable and sparse graph. Unlike existing multi-task…