no code implementations • 24 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.

no code implementations • 28 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.

1 code implementation • 17 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.

no code implementations • 11 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.

no code implementations • 7 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.

no code implementations • 21 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.

no code implementations • 20 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.

no code implementations • 5 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.

no code implementations • 8 Apr 2021 • Mingao Yuan, Zuofeng Shang

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

no code implementations • 12 Jan 2021 • Mingao Yuan, Zuofeng Shang

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

no code implementations • 8 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.

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.

no code implementations • 30 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.

no code implementations • 19 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.

no code implementations • 6 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.

no code implementations • 5 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).

no code implementations • 24 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.

no code implementations • 12 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.

no code implementations • ICML 2018 • Ganggang Xu, Zuofeng Shang, Guang Cheng

Divide-and-conquer is a powerful approach for large and massive data analysis.

no code implementations • 25 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.

no code implementations • 17 Feb 2018 • Meimei Liu, Zuofeng Shang, Guang Cheng

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

no code implementations • ICML 2018 • Ganggang Xu, Zuofeng Shang, Guang Cheng

Tuning parameter selection is of critical importance for kernel ridge regression.

no code implementations • 31 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

no code implementations • 30 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.

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