183 citations · 469 across the 31 of their papers we have counts for
32 papers
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…
Universal computational thermal imaging overcoming the ghosting effect
Hongyi Xu, Du Wang, Chenjun Zhao +6
Thermal imaging is crucial for night vision but fundamentally hampered by the ghosting effect, a loss of detailed texture in cluttered photon streams. While conventional ghosting m…
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…
Skewed Pivot-Blend Modeling with Applications to Semicontinuous Outcomes
Yiyuan She, Xiaoqiang Wu, Lizhu Tao +1
Skewness is a common occurrence in statistical applications. In recent years, various distribution families have been proposed to model skewed data by introducing unequal scales ba…
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…
Analysis of Generalized Bregman Surrogate Algorithms for Nonsmooth Nonconvex Statistical Learning
Yiyuan She, Zhifeng Wang, Jiuwu Jin
Modern statistical applications often involve minimizing an objective function that may be nonsmooth and/or nonconvex. This paper focuses on a broad Bregman-surrogate algorithm fra…
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…
On Generalization and Computation of Tukey's Depth: Part II
Yiyuan She, Shao Tang, Jingze Liu
This paper studies how to generalize Tukey's depth to problems defined in a restricted space that may be curved or have boundaries, and to problems with a nondifferentiable objecti…
On Generalization and Computation of Tukey's Depth: Part I
Yiyuan She, Shao Tang, Jingze Liu
Tukey's depth offers a powerful tool for nonparametric inference and estimation, but also encounters serious computational and methodological difficulties in modern statistical dat…
Gaining Outlier Resistance with Progressive Quantiles: Fast Algorithms and Theoretical Studies
Yiyuan She, Zhifeng Wang, Jiahui Shen
Outliers widely occur in big-data applications and may severely affect statistical estimation and inference. In this paper, a framework of outlier-resistant estimation is introduce…
Network Pruning via Annealing and Direct Sparsity Control
Yangzi Guo, Yiyuan She, Adrian Barbu
Artificial neural networks (ANNs) especially deep convolutional networks are very popular these days and have been proved to successfully offer quite reliable solutions to many vis…
On Cross-validation for Sparse Reduced Rank Regression
Yiyuan She, Hoang Tran
In high-dimensional data analysis, regularization methods pursuing sparsity and/or low rank have received a lot of attention recently. To provide a proper amount of shrinkage, it i…
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…
Iterative proportional scaling revisited: a modern optimization perspective
Yiyuan She, Shao Tang
This paper revisits the classic iterative proportional scaling (IPS) from a modern optimization perspective. In contrast to the criticisms made in the literature, we show that base…
Sparse Generalized Principal Component Analysis for Large-scale Applications beyond Gaussianity
Qiaoya Zhang, Yiyuan She
Principal Component Analysis (PCA) is a dimension reduction technique. It produces inconsistent estimators when the dimensionality is moderate to high, which is often the problem i…
On the Finite-Sample Analysis of -estimators
Yiyuan She
In large-scale modern data analysis, first-order optimization methods are usually favored to obtain sparse estimators in high dimensions. This paper performs theoretical analysis o…
Robust Reduced Rank Regression
Yiyuan She, Kun Chen
In high-dimensional multivariate regression problems, enforcing low rank in the coefficient matrix offers effective dimension reduction, which greatly facilitates parameter estimat…
Joint Association Graph Screening and Decomposition for Large-scale Linear Dynamical Systems
Yiyuan She, Yuejia He, Shijie Li +1
This paper studies large-scale dynamical networks where the current state of the system is a linear transformation of the previous state, contaminated by a multivariate Gaussian no…
Group Regularized Estimation under Structural Hierarchy
Yiyuan She, Zhifeng Wang, He Jiang
Variable selection for models including interactions between explanatory variables often needs to obey certain hierarchical constraints. The weak or strong structural hierarchy req…
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…
Robust Orthogonal Complement Principal Component Analysis
Yiyuan She, Shijie Li, Dapeng Wu
Recently, the robustification of principal component analysis has attracted lots of attention from statisticians, engineers and computer scientists. In this work we study the type…
Selective Factor Extraction in High Dimensions
Yiyuan She
This paper studies simultaneous feature selection and extraction in supervised and unsupervised learning. We propose and investigate selective reduced rank regression for construct…
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…
The Group Square-Root Lasso: Theoretical Properties and Fast Algorithms
Florentina Bunea, Johannes Lederer, Yiyuan She
We introduce and study the Group Square-Root Lasso (GSRL) method for estimation in high dimensional sparse regression models with group structure. The new estimator minimizes the s…