activity
20142026
most citedStatistical Analysis of Nearest Neighbor Methods for Anomaly Detection

50 citations · 84 across the 11 of their papers we have counts for

collaborators

12 papers

cs.CV2026

Deep Learning for Semen Analysis in Male Infertility: Computer Vision, Multimodal Fusion, and Clinical Translation

Runwei Guan, Shaofeng Liang, Jiacheng Weng +10

Male infertility contributes substantially to the global infertility burden, and sperm analysis remains central to diagnosis, treatment planning, and assisted reproductive technolo…

cs.CV2023★ 8 cited

RGI: robust GAN-inversion for mask-free image inpainting and unsupervised pixel-wise anomaly detection

Shancong Mou, Xiaoyi Gu, Meng Cao +4

Generative adversarial networks (GANs), trained on a large-scale image dataset, can be a good approximator of the natural image manifold. GAN-inversion, using a pre-trained generat…

math.OC2022★ 1 cited

Exploiting Instance and Variable Similarity to Improve Learning-Enhanced Branching

Xiaoyi Gu, Santanu S. Dey, Álinson S. Xavier +1

In many operational applications, it is necessary to routinely find, within a very limited time window, provably good solutions to challenging mixed-integer linear programming (MIL…

math.OC2022

Solving sparse separable bilinear programs using lifted bilinear cover inequalities

Xiaoyi Gu, Santanu S. Dey, Jean-Philippe P. Richard

Recently, we proposed a class of inequalities called lifted bilinear cover inequalities, which are second-order cone representable convex inequalities, and are valid for a set desc…

math.OC2021

Lifting convex inequalities for bipartite bilinear programs

Xiaoyi Gu, Santanu S. Dey, Jean-Philippe P. Richard

The goal of this paper is to derive new classes of valid convex inequalities for quadratically constrained quadratic programs (QCQPs) through the technique of lifting. Our first ma…

stat.ML2019★ 50 cited

Statistical Analysis of Nearest Neighbor Methods for Anomaly Detection

Xiaoyi Gu, Leman Akoglu, Alessandro Rinaldo

Nearest-neighbor (NN) procedures are well studied and widely used in both supervised and unsupervised learning problems. In this paper we are concerned with investigating the perfo…

math.OC2019

Exact Augmented Lagrangian Duality for Mixed Integer Quadratic Programming

Xiaoyi Gu, Shabbir Ahmed, Santanu S. Dey

Mixed integer quadratic programming (MIQP) is the problem of minimizing a convex quadratic function over mixed integer points in a rational polyhedron. This paper focuses on the au…

math.OC2017★ 16 cited

Global Optimization with Orthogonality Constraints via Stochastic Diffusion on Manifold

Honglin Yuan, Xiaoyi Gu, Rongjie Lai +1

Orthogonality constrained optimization is widely used in applications from science and engineering. Due to the nonconvex orthogonality constraints, many numerical algorithms often…

cs.IT2016★ 3 cited

Optimizing quantization for Lasso recovery

Xiaoyi Gu, Shenyinying Tu, Hao-Jun Michael Shi +3

This letter is focused on quantized Compressed Sensing, assuming that Lasso is used for signal estimation. Leveraging recent work, we provide a framework to optimize the quantizati…

cs.IT2015

Methods for Quantized Compressed Sensing

Hao-Jun Michael Shi, Mindy Case, Xiaoyi Gu +2

In this paper, we compare and catalog the performance of various greedy quantized compressed sensing algorithms that reconstruct sparse signals from quantized compressed measuremen…

math.NA2015

A note on practical approximate projection schemes in signal space methods

Xiaoyi Gu, Deanna Needell, Shenyinying Tu

Compressive sensing (CS) is a new technology which allows the acquisition of signals directly in compressed form, using far fewer measurements than traditional theory dictates. Rec…

math.NA2014★ 6 cited

Practical approximate projection schemes in greedy signal space methods

Chris Garnatz, Xiaoyi Gu, Alison Kingman +3

Compressive sensing (CS) is a new signal acquisition paradigm which shows that far fewer samples are required to reconstruct sparse signals than previously thought. Although most o…