39 citations · 157 across the 35 of their papers we have counts for
5 papers · 1 filter
Decomposed Structured Subsets for Semidefinite and Sum-of-Squares Optimization
Jared Miller, Yang Zheng, Mario Sznaier +1
Semidefinite programs (SDPs) are standard convex problems that are frequently found in control and optimization applications. Interior-point methods can solve SDPs in polynomial ti…
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…
Iterative Spectral Method for Alternative Clustering
Chieh Wu, Stratis Ioannidis, Mario Sznaier +3
Given a dataset and an existing clustering as input, alternative clustering aims to find an alternative partition. One of the state-of-the-art approaches is Kernel Dimension Altern…
Spectral Non-Convex Optimization for Dimension Reduction with Hilbert-Schmidt Independence Criterion
Chieh Wu, Jared Miller, Yale Chang +2
The Hilbert Schmidt Independence Criterion (HSIC) is a kernel dependence measure that has applications in various aspects of machine learning. Conveniently, the objectives of diffe…
Chordal Decomposition in Rank Minimized Semidefinite Programs with Applications to Subspace Clustering
Jared Miller, Yang Zheng, Biel Roig-Solvas +2
Semidefinite programs (SDPs) often arise in relaxations of some NP-hard problems, and if the solution of the SDP obeys certain rank constraints, the relaxation will be tight. Decom…