L
Lexing Ying
Researcher at Stanford University
Publications - 285
Citations - 10563
Lexing Ying is an academic researcher from Stanford University. The author has contributed to research in topics: Preconditioner & Computer science. The author has an hindex of 45, co-authored 250 publications receiving 9213 citations. Previous affiliations of Lexing Ying include California Institute of Technology & Facebook.
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Analytic continuation from limited noisy Matsubara data
TL;DR: In this paper , a new algorithm for estimating spectral function from limited noisy Matsubara data is proposed for both the molecule and condensed matter cases, where the algorithm constructs an interpolant of the data and uses conformal mapping and Prony's method to estimate the spectral function.
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Solving for high dimensional committor functions using neural network with online approximation to derivatives.
TL;DR: A new method based on neural networks for computing the high-dimensional committor functions that satisfy Fokker-Planck equations is proposed, which means stochastic gradient descent type algorithms can be applied in the training of the committors without the need of computing any second-order derivatives.
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Maximizing robustness of point-set registration by leveraging non-convexity
TL;DR: It is shown that if the fraction of outliers is larger than a certain threshold, any naive convex relaxation fails to recover the ground truth rotation regardless of the sample size and dimension, and minimizing the least unsquared deviation directly over the special orthogonal group exactly recovers the groundtruth rotation for any level of corruption.
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Computing localized representations of the kohn-sham subspace via randomization and refinement
Anil Damle,Lin Lin,Lexing Ying +2 more
TL;DR: In this article, a two-stage approximate column selection strategy is proposed to find the important columns at much lower computational cost, which is more efficient than column pivoted QR factorization.
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Directional Preconditioner for 2D High Frequency Obstacle Scattering
TL;DR: This paper presents the directional preconditioner for the iterative solution of linear systems of the boundary integral method, which builds a data-sparse approximation of the integral operator, transforms it into a sparse linear system, and computes an approximate inverse with efficient sparse and hierarchical linear algebra algorithms.