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20012023
most citedTraining Set Debugging Using Trusted Items

26 citations · 56 across the 17 of their papers we have counts for

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Showing 2019Show all

8 papers · 1 filter

cs.LG2019

Interleaved Composite Quantization for High-Dimensional Similarity Search

Soroosh Khoram, Stephen J Wright, Jing Li

Similarity search retrieves the nearest neighbors of a query vector from a dataset of high-dimensional vectors. As the size of the dataset grows, the cost of performing the distanc…

cs.LG20191 cited

A Distributed Quasi-Newton Algorithm for Primal and Dual Regularized Empirical Risk Minimization

Ching-pei Lee, Cong Han Lim, Stephen J. Wright

We propose a communication- and computation-efficient distributed optimization algorithm using second-order information for solving empirical risk minimization (ERM) problems with…

math.OC2019

Trust-Region Newton-CG with Strong Second-Order Complexity Guarantees for Nonconvex Optimization

Frank E. Curtis, Daniel P. Robinson, Clément Royer +1

Worst-case complexity guarantees for nonconvex optimization algorithms have been a topic of growing interest. Multiple frameworks that achieve the best known complexity bounds amon…

math.NA20191 cited

Schwarz iteration method for elliptic equation with rough media based on random sampling

Ke Chen, Qin Li, Stephen J. Wright

We propose a computationally efficient Schwarz method for elliptic equations with rough media. A random sampling strategy is used to find low-rank approximations of all local solut…

math.NA2019

Structured random sketching for PDE inverse problems

Ke Chen, Qin Li, Kit Newton +1

For an overdetermined system with and given, the least-square (LS) formulation $\min_x \, \|\mathsf{A}\mathsf{x}…

math.OC2019

Complexity of Proximal augmented Lagrangian for nonconvex optimization with nonlinear equality constraints

Yue Xie, Stephen J. Wright

We analyze worst-case complexity of a Proximal augmented Lagrangian (Proximal AL) framework for nonconvex optimization with nonlinear equality constraints. When an approximate firs…