4 citations · 4 across the 1 of their papers we have counts for
2 papers
stat.ML2019★ 4 cited
Solving Interpretable Kernel Dimension Reduction
Chieh Wu, Jared Miller, Yale Chang +2
Kernel dimensionality reduction (KDR) algorithms find a low dimensional representation of the original data by optimizing kernel dependency measures that are capable of capturing n…
cs.LG2019
Deep Kernel Learning for Clustering
Chieh Wu, Zulqarnain Khan, Yale Chang +2
We propose a deep learning approach for discovering kernels tailored to identifying clusters over sample data. Our neural network produces sample embeddings that are motivated by--…