Lin Lin

Conference Proceedings of Machine Learning Research · January 1, 2023 Learning multi-view data is an emerging problem in machine learning research, and nonnegative matrix factorization (NMF) is a popular dimensionality-reduction method for integrating information from multiple views. These views often provide not only consen ... Cite

Probabilistic Model Incorporating Auxiliary Covariates to Control FDR

Conference International Conference on Information and Knowledge Management Proceedings · October 17, 2022 Controlling False Discovery Rate (FDR) while leveraging the side information of multiple hypothesis testing is an emerging research topic in modern data science. Existing methods rely on the test-level covariates while ignoring metrics about test-level cov ... Full text Cite

Interpretable Representation Learning from Temporal Multi-view Data

Conference Proceedings of Machine Learning Research · January 1, 2022 In many scientific problems such as video surveillance, modern genomics, and finance, data are often collected from diverse measurements across time that exhibit time-dependent heterogeneous properties. Thus, it is important to not only integrate data from ... Cite

Lin Lin

Scholarly Works - Conferences

Adaptive Weighted Multi-View Clustering

Probabilistic Model Incorporating Auxiliary Covariates to Control FDR

Interpretable Representation Learning from Temporal Multi-view Data

Defending Against Adversarial Samples Without Security through Obscurity

Explaining Deep Learning Models - A Bayesian Non-parametric Approach

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