1 citations · 3 across the 14 of their papers we have counts for
15 papers
Bandable Cumulant Tensors: Optimal Estimation and Applications in Non-Gaussian Data Modeling
Runshi Tang, Anru R. Zhang, Yuefeng Han +1
Higher-order cumulants capture the non-Gaussian dependence that covariance misses, but they are hard to use in high dimensions. An order- cumulant tensor has entries, and…
Stability beyond Bounded Differences: Sharp Generalization Bounds under Finite Moments
Qianqian Lei, Soham Bonnerjee, Yuefeng Han +1
While algorithmic stability is a central tool for understanding generalization of learning algorithms, existing high-probability guarantees typically rely on uniform boundedness or…
Detection Is Harder Than Estimation in Certain Regimes: Inference for Moment and Cumulant Tensors
Runshi Tang, Yuefeng Han, Anru R. Zhang
We study estimation and detection of high-order cumulant tensors from i.i.d. observations of a -dimensional random vector, with performance measured in the tensor spectral n…
From Reverse Detection--Estimation Gaps to Computational Lower Bounds for Norm Approximation
Runshi Tang, Yuefeng Han, Anru R. Zhang
We develop a general reduction scheme that converts reverse detection--estimation gaps into computational lower bounds for norm approximation. If an efficiently computable estimato…
Panel Coupled Matrix-Tensor Clustering Model with Applications to Asset Pricing
Liyuan Cui, Guanhao Feng, Yuefeng Han +1
We tackle the challenge of estimating grouping structures and factor loadings in asset pricing models, where traditional regressions struggle due to sparse data and high noise. Exi…
High-Dimensional Tensor Discriminant Analysis: Low-Rank Discriminant Structure, Representation Synergy, and Theoretical Guarantees
Elynn Chen, Yuefeng Han, Jiayu Li
High-dimensional tensor-valued predictors arise in modern applications, increasingly as learned representations from neural networks. Existing tensor classification methods rely on…
Tensor Neyman-Pearson Classification: Theory, Algorithms, and Error Control
Lingchong Liu, Elynn Chen, Yuefeng Han +1
Biochemical discovery increasingly relies on classifying molecular structures when the consequences of different errors are highly asymmetric. In mutagenicity and carcinogenicity,…
Modewise Additive Factor Model for Matrix Time Series
Elynn Chen, Yuefeng Han, Jiayu Li +1
We introduce a Modewise Additive Factor Model (MAFM) for matrix-valued time series that captures row-specific and column-specific latent effects through an additive structure, offe…
Guaranteed Noisy CP Tensor Recovery via Riemannian Optimization on the Segre Manifold
Ke Xu, Yuefeng Han
Recovering a low-CP-rank tensor from noisy linear measurements is a central challenge in high-dimensional data analysis, with applications spanning tensor PCA, tensor regression, a…
WISE: A Weighted Similarity Aggregation Test for Serial Independence
Qihua Zhu, Mingshuo Liu, Yuefeng Han +1
We propose a nonparametric test for serial independence that aggregates pairwise similarities of observations with lag-dependent weights. The resulting statistic is powerful to gen…
Statistical Inference for Low-Rank Tensor Models
Ke Xu, Elynn Chen, Yuefeng Han
Statistical inference for tensors has emerged as a critical challenge in analyzing high-dimensional data in modern data science. This paper introduces a unified framework for infer…
High-Dimensional Tensor Discriminant Analysis with Incomplete Tensors
Elynn Chen, Yuefeng Han, Jiayu Li
Tensor classification is gaining importance across fields, yet handling partially observed data remains challenging. In this paper, we introduce a novel approach to tensor classifi…
TEAFormers: TEnsor-Augmented Transformers for Multi-Dimensional Time Series Forecasting
Linghang Kong, Elynn Chen, Yuzhou Chen +1
Multi-dimensional time series data, such as matrix and tensor-variate time series, are increasingly prevalent in fields such as economics, finance, and climate science. Traditional…
High-Dimensional Tensor Classification with CP Low-Rank Discriminant Structure
Elynn Chen, Yuefeng Han, Jiayu Li
Tensor classification has become increasingly crucial in statistics and machine learning, with applications spanning neuroimaging, computer vision, and recommendation systems. Howe…
Factor Augmented Tensor-on-Tensor Neural Networks
Guanhao Zhou, Yuefeng Han, Xiufan Yu
This paper studies the prediction task of tensor-on-tensor regression in which both covariates and responses are multi-dimensional arrays (a.k.a., tensors) across time with arbitra…