Search Results for author: Zuofeng Shang

Found 24 papers, 1 papers with code

Statistical Inference with Stochastic Gradient Methods under $φ$-mixing Data

no code implementations24 Feb 2023 Ruiqi Liu, Xi Chen, Zuofeng Shang

In this paper, we propose a mini-batch SGD estimator for statistical inference when the data is $\phi$-mixing.

Time Series Time Series Analysis +1

Solar Flare Index Prediction Using SDO/HMI Vector Magnetic Data Products with Statistical and Machine Learning Methods

no code implementations28 Sep 2022 Hewei Zhang, Qin Li, Yanxing Yang, Ju Jing, Jason T. L. Wang, Haimin Wang, Zuofeng Shang

In addition, we sort the importance of SHARP parameters by Borda Count method calculated from the ranks that are rendered by 9 different machine learning methods.

regression

Deep Neural Network Classifier for Multi-dimensional Functional Data

1 code implementation17 May 2022 Shuoyang Wang, Guanqun Cao, Zuofeng Shang

We propose a new approach, called as functional deep neural network (FDNN), for classifying multi-dimensional functional data.

Statistical Limits for Testing Correlation of Hypergraphs

no code implementations11 Feb 2022 Mingao Yuan, Zuofeng Shang

In this paper, we consider the hypothesis testing of correlation between two $m$-uniform hypergraphs on $n$ unlabelled nodes.

Calibrating multi-dimensional complex ODE from noisy data via deep neural networks

no code implementations7 Jun 2021 Kexuan Li, Fangfang Wang, Ruiqi Liu, Fan Yang, Zuofeng Shang

Our method is able to recover the ODE system without being subject to the curse of dimensionality and complicated ODE structure.

Online Statistical Inference for Parameters Estimation with Linear-Equality Constraints

no code implementations21 May 2021 Ruiqi Liu, Mingao Yuan, Zuofeng Shang

Stochastic gradient descent (SGD) and projected stochastic gradient descent (PSGD) are scalable algorithms to compute model parameters in unconstrained and constrained optimization problems.

Distributed Adaptive Nearest Neighbor Classifier: Algorithm and Theory

no code implementations20 May 2021 Ruiqi Liu, Ganggang Xu, Zuofeng Shang

When data is of an extraordinarily large size or physically stored in different locations, the distributed nearest neighbor (NN) classifier is an attractive tool for classification.

Information Limits for Detecting a Subhypergraph

no code implementations5 May 2021 Mingao Yuan, Zuofeng Shang

We consider the problem of recovering a subhypergraph based on an observed adjacency tensor corresponding to a uniform hypergraph.

Heterogeneous Dense Subhypergraph Detection

no code implementations8 Apr 2021 Mingao Yuan, Zuofeng Shang

We study the problem of testing the existence of a heterogeneous dense subhypergraph.

Sharp detection boundaries on testing dense subhypergraph

no code implementations12 Jan 2021 Mingao Yuan, Zuofeng Shang

In both scenarios, sharp detectable boundaries are characterized by the appropriate model parameters.

Estimation of the Mean Function of Functional Data via Deep Neural Networks

no code implementations8 Dec 2020 Shuoyang Wang, Guanqun Cao, Zuofeng Shang

In this work, we propose a deep neural network method to perform nonparametric regression for functional data.

regression

Probabilistic Connection Importance Inference and Lossless Compression of Deep Neural Networks

no code implementations ICLR 2020 Xin Xing, Long Sha, Pengyu Hong, Zuofeng Shang, Jun S. Liu

Deep neural networks (DNNs) can be huge in size, requiring a considerable a mount of energy and computational resources to operate, which limits their applications in numerous scenarios.

On Deep Instrumental Variables Estimate

no code implementations30 Apr 2020 Ruiqi Liu, Zuofeng Shang, Guang Cheng

The endogeneity issue is fundamentally important as many empirical applications may suffer from the omission of explanatory variables, measurement error, or simultaneous causality.

Sharp Rate of Convergence for Deep Neural Network Classifiers under the Teacher-Student Setting

no code implementations19 Jan 2020 Tianyang Hu, Zuofeng Shang, Guang Cheng

In this paper, we attempt to understand this empirical success in high dimensional classification by deriving the convergence rates of excess risk.

General Classification

Minimax Nonparametric Two-sample Test under Smoothing

no code implementations6 Nov 2019 Xin Xing, Zuofeng Shang, Pang Du, Ping Ma, Wenxuan Zhong, Jun S. Liu

Under such a framework, the probability density comparison is equivalent to testing the presence/absence of interactions.

Two-sample testing Vocal Bursts Valence Prediction

Optimal Nonparametric Inference via Deep Neural Network

no code implementations5 Feb 2019 Ruiqi Liu, Ben Boukai, Zuofeng Shang

Sufficient conditions on network architectures are provided such that the upper bounds become optimal (without log-sacrifice).

Two-sample testing

Nonparametric Inference under B-bits Quantization

no code implementations24 Jan 2019 Kexuan Li, Ruiqi Liu, Ganggang Xu, Zuofeng Shang

Statistical inference based on lossy or incomplete samples is often needed in research areas such as signal/image processing, medical image storage, remote sensing, signal transmission.

Quantization

A likelihood-ratio type test for stochastic block models with bounded degrees

no code implementations12 Jul 2018 Mingao Yuan, Yang Feng, Zuofeng Shang

A fundamental problem in network data analysis is to test Erd\"{o}s-R\'{e}nyi model $\mathcal{G}\left(n,\frac{a+b}{2n}\right)$ versus a bisection stochastic block model $\mathcal{G}\left(n,\frac{a}{n},\frac{b}{n}\right)$, where $a, b>0$ are constants that represent the expected degrees of the graphs and $n$ denotes the number of nodes.

Community Detection Stochastic Block Model

How Many Machines Can We Use in Parallel Computing for Kernel Ridge Regression?

no code implementations25 May 2018 Meimei Liu, Zuofeng Shang, Guang Cheng

It is worth noting that the upper bounds of the number of machines are proven to be un-improvable (upto a logarithmic factor) in two important cases: smoothing spline regression and Gaussian RKHS regression.

regression Two-sample testing

Nonparametric Testing under Random Projection

no code implementations17 Feb 2018 Meimei Liu, Zuofeng Shang, Guang Cheng

A common challenge in nonparametric inference is its high computational complexity when data volume is large.

regression

Computational Limits of A Distributed Algorithm For Smoothing Spline

no code implementations31 Dec 2015 Zuofeng Shang, Guang Cheng

In this paper, we explore statistical versus computational trade-off to address a basic question in the application of a distributed algorithm: what is the minimal computational cost in obtaining statistical optimality?

Statistics Theory Statistics Theory

Local and global asymptotic inference in smoothing spline models

no code implementations30 Dec 2012 Zuofeng Shang, Guang Cheng

In particular, our confidence intervals are proved to be asymptotically valid at any point in the support, and they are shorter on average than the Bayesian confidence intervals proposed by Wahba [J. R. Stat.

Math valid

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