197 citations · 591 across the 57 of their papers we have counts for
62 papers
Accelerated Learning of High Dimensional Functions with a Tensor-Featured Training Network
Karl Pierce, Yuehaw Khoo, Haizhao Yang
In this work we present a method to accelerate the optimization of learning high dimensional functions using deep neural network (DNN). This optimization procedure introduces conte…
Cluster-Aware Matching via Laplacian Optimal Transport
Gabriel Samberg, YoonHaeng Hur, Yuehaw Khoo +1
In many applications of matching, the point clouds to be matched are not merely unstructured sets of points but rather samples from distributions with an intrinsic cluster structur…
Convex Relaxations for the Optimization of Markov Processes
Hongyi Zhang, Yuehaw Khoo, Tianyun Tang
In this paper, we study the problem of optimizing Markov processes that interpolate between two prescribed probability distributions while minimizing a given cost. The main computa…
Inlier Recovery for Robust Registration via Gram-Matrix Overlap
Ruizi Wu, Yuehaw Khoo, Wanjie Wang
Robust point-set registration in the presence of noise and outliers is challenging because the matched points (inliers) must be identified before reliable alignment can be performe…
Non-Uniform Quantum Fourier Transform
Junaid Aftab, Yuehaw Khoo, Haizhao Yang
The Discrete Fourier Transform (DFT) is central to the analysis of uniformly sampled signals, yet many practical applications involve non-uniform sampling, requiring the Non-Unifor…
Adaptive tensor train metadynamics for high-dimensional free energy exploration
Nils E. Strand, Siyao Yang, Yuehaw Khoo +1
A key challenge for molecular dynamics simulations is efficient exploration of free energy landscapes over relevant collective variables (CV). Common methods for enhancing sampling…
Recursive Sketched Interpolation: Efficient Hadamard Products of Tensor Trains
Zhaonan Meng, Yuehaw Khoo, Jiajia Li +1
The Hadamard product of two tensors in the tensor-train (TT) format is a fundamental operation across various applications, such as TT-based function multiplication for nonlinear d…
Fast operator learning for mapping correlations
Yuehaw Khoo, Yuguan Wang, Siyao Yang
We propose a fast, optimization-free method for learning the transition operators of high-dimensional Markov processes. The central idea is to perform a Galerkin projection of the…
A Model-Guided Neural Network Method for the Inverse Scattering Problem
Olivia Tsang, Owen Melia, Vasileios Charisopoulos +3
Inverse medium scattering is an ill-posed, nonlinear wave-based imaging problem arising in medical imaging, remote sensing, and non-destructive testing. Machine learning (ML) metho…
Sketch Tomography: Hybridizing Classical Shadow and Matrix Product State
Xun Tang, Haoxuan Chen, Yuehaw Khoo +1
We introduce Sketch Tomography, an efficient procedure for quantum state tomography based on the classical shadow protocol used for quantum observable estimations. The procedure ap…
Convex relaxation approaches for high-dimensional optimal transport
Yuehaw Khoo, Tianyun Tang
Optimal transport (OT) is a powerful tool in mathematics and data science but faces severe computational and statistical challenges in high dimensions. We propose convex relaxation…
Optimization-Free Diffusion Model -- A Perturbation Theory Approach
Yuehaw Khoo, Mathias Oster, Yifan Peng
Diffusion models have emerged as a powerful framework in generative modeling, typically relying on optimizing neural networks to estimate the score function via forward SDE simulat…
On a spherically lifted spin model at finite temperature
Xun Tang, Yuehaw Khoo, Lexing Ying
We investigate an \(n\)-vector model over \(k\) sites with generic pairwise interactions and spherical constraints. The model is a lifting of the Ising model whereby the support of…
Initialization and training of matrix product state probabilistic models
Xun Tang, Yuehaw Khoo, Lexing Ying
Modeling probability distributions via the wave function of a quantum state is central to quantum-inspired generative modeling and quantum state tomography (QST). We investigate a…
A Bregman ADMM for Bethe variational problem
Yuehaw Khoo, Tianyun Tang, Kim-Chuan Toh
In this work, we propose a novel Bregman ADMM with nonlinear dual update to solve the Bethe variational problem (BVP), a key optimization formulation in graphical models and statis…
Tensor Density Estimator by Convolution-Deconvolution
Yifan Peng, Siyao Yang, Yuehaw Khoo +1
We propose a linear algebraic framework for performing density estimation. It consists of three simple steps: convolving the empirical distribution with certain smoothing kernels t…
Re-anchoring Quantum Monte Carlo with Tensor-Train Sketching
Ziang Yu, Shiwei Zhang, Yuehaw Khoo
We propose a novel algorithm for calculating the ground-state energy of quantum many-body systems by combining auxiliary-field quantum Monte Carlo (AFQMC) with tensor-train sketchi…
Method of Moments for Estimation of Noisy Curves
Phillip Lo, Yuehaw Khoo
In this paper, we study the problem of recovering a ground truth high dimensional piecewise linear curve from a high noise Gaussian point cloud with…
Subspace method of moments for ab initio 3-D single-particle cryo-EM reconstruction
Jeremy Hoskins, Yuehaw Khoo, Oscar Mickelin +2
Cryo-electron microscopy (cryo-EM) is a widely used technique for recovering the 3-D structure of biological molecules from a large number of experimentally generated noisy 2-D tom…
Solving cluster moment relaxation with hierarchical matrix
Yi Wang, Rizheng Huang, Yuehaw Khoo
Convex relaxation methods are powerful tools for studying the lowest energy of many-body problems. By relaxing the representability conditions for marginals to a set of local const…
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…
Permutation Recovery on Manifold Data via Spectral Seriation
Yuehaw Khoo, Xin T. Tong, Wanjie Wang +1
Data points in many scientific experiments originate from an ordered structure, yet this ordering is often unavailable.We consider noisy data points with the correct ordering to be…
Multi-Frequency Progressive Refinement for Learned Inverse Scattering
Owen Melia, Olivia Tsang, Vasileios Charisopoulos +3
Interpreting scattered acoustic and electromagnetic wave patterns is a computational task that enables remote imaging in a number of important applications, including medical imagi…
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…