12 citations · 15 across the 6 of their papers we have counts for
6 papers
S-SOS: Stochastic Sum-Of-Squares for Parametric Polynomial Optimization
Richard L. Zhu, Mathias Oster, Yuehaw Khoo
Global polynomial optimization is an important tool across applied mathematics, with many applications in operations research, engineering, and physical sciences. In various settin…
Augmented Lagrangian method for coupled-cluster
Fabian M. Faulstich, Yuehaw Khoo, Kangbo Li
We propose to improve the convergence properties of the single-reference coupled cluster (CC) method through an augmented Lagrangian formalism. The conventional CC method changes a…
Tensorizing flows: a tool for variational inference
Yuehaw Khoo, Michael Lindsey, Hongli Zhao
Fueled by the expressive power of deep neural networks, normalizing flows have achieved spectacular success in generative modeling, or learning to draw new samples from a distribut…
Generative Modeling via Tree Tensor Network States
Xun Tang, Yoonhaeng Hur, Yuehaw Khoo +1
In this paper, we present a density estimation framework based on tree tensor-network states. The proposed method consists of determining the tree topology with Chow-Liu algorithm,…
Quantitatively visualizing bipartite datasets
Tal Einav, Yuehaw Khoo, Amit Singer
As experiments continue to increase in size and scope, a fundamental challenge of subsequent analyses is to recast the wealth of information into an intuitive and readily-interpret…
Open problem: Tightness of maximum likelihood semidefinite relaxations
Afonso S. Bandeira, Yuehaw Khoo, Amit Singer
We have observed an interesting, yet unexplained, phenomenon: Semidefinite programming (SDP) based relaxations of maximum likelihood estimators (MLE) tend to be tight in recovery p…