27 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…
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…
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…
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…