183 citations · 469 across the 31 of their papers we have counts for
10 papers · 1 filter
Range Penalization: Theoretical Insights with Applications in Federated Learning
Yiyuan She, Zhaojun Hu, Yifan Sun
This paper introduces range regularization for federated learning with linear systematic components to enhance statistical accuracy and induce cross-client regularity conducive to…
Mind the Jumps: A Scalable Robust Local Gaussian Process for Multidimensional Response Surfaces with Discontinuities
Isaac Adjetey, Yiyuan She
Modeling response surfaces with abrupt jumps and discontinuities remains a major challenge across scientific and engineering domains. Although Gaussian process models excel at capt…
Slow Kill for Big Data Learning
Yiyuan She, Jianhui Shen, Adrian Barbu
Big-data applications often involve a vast number of observations and features, creating new challenges for variable selection and parameter estimation. This paper presents a novel…
Supervised Multivariate Learning with Simultaneous Feature Auto-grouping and Dimension Reduction
Yiyuan She, Jiahui Shen, Chao Zhang
Modern high-dimensional methods often adopt the "bet on sparsity" principle, while in supervised multivariate learning statisticians may face "dense" problems with a large number o…
Indirect Gaussian Graph Learning beyond Gaussianity
Yiyuan She, Shao Tang, Qiaoya Zhang
This paper studies how to capture dependency graph structures from real data which may not be Gaussian. Starting from marginal loss functions not necessarily derived from probabili…
Learning Topology and Dynamics of Large Recurrent Neural Networks
Yiyuan She, Yuejia He, Dapeng Wu
Large-scale recurrent networks have drawn increasing attention recently because of their capabilities in modeling a large variety of real-world phenomena and physical mechanisms. T…
Feature Selection with Annealing for Computer Vision and Big Data Learning
Adrian Barbu, Yiyuan She, Liangjing Ding +1
Many computer vision and medical imaging problems are faced with learning from large-scale datasets, with millions of observations and features. In this paper we propose a novel ef…
Group Iterative Spectrum Thresholding for Super-Resolution Sparse Spectral Selection
Yiyuan She, Huanghuang Li, Jiangping Wang +1
Recently, sparsity-based algorithms are proposed for super-resolution spectrum estimation. However, to achieve adequately high resolution in real-world signal analysis, the diction…
Reduced Rank Vector Generalized Linear Models for Feature Extraction
Yiyuan She
Supervised linear feature extraction can be achieved by fitting a reduced rank multivariate model. This paper studies rank penalized and rank constrained vector generalized linear…
An Iterative Algorithm for Fitting Nonconvex Penalized Generalized Linear Models with Grouped Predictors
Yiyuan She
High-dimensional data pose challenges in statistical learning and modeling. Sometimes the predictors can be naturally grouped where pursuing the between-group sparsity is desired.…