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20182022
most citedHow to Learn when Data Reacts to Your Model: Performative Gradient Descent

11 citations · 42 across the 26 of their papers we have counts for

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17 papers · 1 filter

math.NA2022

Annealed importance sampling for Ising models with mixed boundary conditions

Lexing Ying

This note introduces a method for sampling Ising models with mixed boundary conditions. As an application of annealed importance sampling and the Swendsen-Wang algorithm, the metho…

math.NA2022

Double Flip Move for Ising Models with Mixed Boundary Conditions

Lexing Ying

This note introduces the double flip move for accelerating the Swendsen-Wang algorithm for Ising models with mixed boundary conditions below the critical temperature. The double fl…

math.NA20221 cited

Pole recovery from noisy data on imaginary axis

Lexing Ying

This note proposes an algorithm for identifying the poles and residues of a meromorphic function from its noisy values on the imaginary axis. The algorithm uses Möbius transform an…

math.NA2021

Approximate inversion of discrete Fourier integral operators

Jordi Feliu-Fabà, Lexing Ying

This paper introduces a factorization for the inverse of discrete Fourier integral operators that can be applied in quasi-linear time. The factorization starts by approximating the…

math.NA2021

Multi-Level Fine-Tuning: Closing Generalization Gaps in Approximation of Solution Maps under a Limited Budget for Training Data

Zhihan Li, Yuwei Fan, Lexing Ying

In scientific machine learning, regression networks have been recently applied to approximate solution maps (e.g., potential-ground state map of Schrödinger equation). In this pape…

math.NA2021

An efficient dynamical low-rank algorithm for the Boltzmann-BGK equation close to the compressible viscous flow regime

Lukas Einkemmer, Jingwei Hu, Lexing Ying

It has recently been demonstrated that dynamical low-rank algorithms can provide robust and efficient approximation to a range of kinetic equations. This is true especially if the…