arXiv subjects

Jingfei Zhang

Publications and source records attributed to Jingfei Zhang.

At least 37 records · Page 2Linked to original sources

arXiv2022

Multi-task Learning for Gaussian Graphical Regressions with High Dimensional Covariates

Gaussian graphical regression is a powerful means that regresses the precision matrix of a Gaussian graphical model on covariates, permitting the numbers of the response variables and covariates to far exceed the sample size. Model fitting is typically carried out via separate node-wise lasso regressions, ignoring the network-induced structure among these regressions. Consequently, the error rate is high, especially when the number of nodes is large. We propose a multi-task learning estimator for fitting Gaussian graphical regression models; we design a cross-task group sparsity penalty and a within task element-wise sparsity penalty, which govern the sparsity of active covariates and their effects on the graph, respectively. For computation, we consider an efficient augmented Lagrangian algorithm, which solves subproblems with a semi-smooth Newton method. For theory, we show that the error rate of the multi-task learning based estimates has much improvement over that of the separate node-wise lasso estimates, because the cross-task penalty borrows information across tasks. To address the main challenge that the tasks are entangled in a complicated correlation structure, we establish a new tail probability bound for correlated heavy-tailed (sub-exponential) variables with an arbitrary correlation structure, a useful theoretical result in its own right. Finally, the utility of our method is demonstrated through simulations as well as an application to a gene co-expression network study with brain cancer patients.

stat.ME↗

arXiv2022

High Dimensional Gaussian Graphical Regression Models with Covariates

Though Gaussian graphical models have been widely used in many scientific fields, relatively limited progress has been made to link graph structures to external covariates. We propose a Gaussian graphical regression model, which regresses both the mean and the precision matrix of a Gaussian graphical model on covariates. In the context of co-expression quantitative trait locus (QTL) studies, our method can determine how genetic variants and clinical conditions modulate the subject-level network structures, and recover both the population-level and subject-level gene networks. Our framework encourages sparsity of covariate effects on both the mean and the precision matrix. In particular for the precision matrix, we stipulate simultaneous sparsity, i.e., group sparsity and element-wise sparsity, on effective covariates and their effects on network edges, respectively. We establish variable selection consistency first under the case with known mean parameters and then a more challenging case with unknown means depending on external covariates, and establish in both cases the $\ell_2$ convergence rates and the selection consistency of the estimated precision parameters. The utility and efficacy of our proposed method is demonstrated through simulation studies and an application to a co-expression QTL study with brain cancer patients.

stat.ME↗

arXiv2021

Learning Human Activity Patterns using Clustered Point Processes with Active and Inactive States

Modeling event patterns is a central task in a wide range of disciplines. In applications such as studying human activity patterns, events often arrive clustered with sporadic and long periods of inactivity. Such heterogeneity in event patterns poses challenges for existing point process models. In this article, we propose a new class of clustered point processes that alternate between active and inactive states. The proposed model is flexible, highly interpretable, and can provide useful insights into event patterns. A composite likelihood approach and a composite EM estimation procedure are developed for efficient and numerically stable parameter estimation. We study both the computational and statistical properties of the estimator including convergence, consistency, and asymptotic normality. The proposed method is applied to Donald Trump's Twitter data to investigate if and how his behaviors evolved before, during, and after the presidential campaign. Additionally, we analyze large-scale social media data from Sina Weibo and identify interesting groups of users with distinct behaviors.

stat.AP↗

arXiv2021

Fast Network Community Detection with Profile-Pseudo Likelihood Methods

The stochastic block model is one of the most studied network models for community detection. It is well-known that most algorithms proposed for fitting the stochastic block model likelihood function cannot scale to large-scale networks. One prominent work that overcomes this computational challenge is Amini et al.(2013), which proposed a fast pseudo-likelihood approach for fitting stochastic block models to large sparse networks. However, this approach does not have convergence guarantee, and is not well suited for small- or medium- scale networks. In this article, we propose a novel likelihood based approach that decouples row and column labels in the likelihood function, which enables a fast alternating maximization; the new method is computationally efficient, performs well for both small and large scale networks, and has provable convergence guarantee. We show that our method provides strongly consistent estimates of the communities in a stochastic block model. As demonstrated in simulation studies, the proposed method outperforms the pseudo-likelihood approach in terms of both estimation accuracy and computation efficiency, especially for large sparse networks. We further consider extensions of our proposed method to handle networks with degree heterogeneity and bipartite properties.

stat.ME↗

arXiv2021

Discriminative-Generative Representation Learning for One-Class Anomaly Detection

As a kind of generative self-supervised learning methods, generative adversarial nets have been widely studied in the field of anomaly detection. However, the representation learning ability of the generator is limited since it pays too much attention to pixel-level details, and generator is difficult to learn abstract semantic representations from label prediction pretext tasks as effective as discriminator. In order to improve the representation learning ability of generator, we propose a self-supervised learning framework combining generative methods and discriminative methods. The generator no longer learns representation by reconstruction error, but the guidance of discriminator, and could benefit from pretext tasks designed for discriminative methods. Our discriminative-generative representation learning method has performance close to discriminative methods and has a great advantage in speed. Our method used in one-class anomaly detection task significantly outperforms several state-of-the-arts on multiple benchmark data sets, increases the performance of the top-performing GAN-based baseline by 6% on CIFAR-10 and 2% on MVTAD.

cs.CV↗

arXiv2021

Partially Observed Dynamic Tensor Response Regression

In modern data science, dynamic tensor data is prevailing in numerous applications. An important task is to characterize the relationship between such dynamic tensor and external covariates. However, the tensor data is often only partially observed, rendering many existing methods inapplicable. In this article, we develop a regression model with partially observed dynamic tensor as the response and external covariates as the predictor. We introduce the low-rank, sparsity and fusion structures on the regression coefficient tensor, and consider a loss function projected over the observed entries. We develop an efficient non-convex alternating updating algorithm, and derive the finite-sample error bound of the actual estimator from each step of our optimization algorithm. Unobserved entries in tensor response have imposed serious challenges. As a result, our proposal differs considerably in terms of estimation algorithm, regularity conditions, as well as theoretical properties, compared to the existing tensor completion or tensor response regression solutions. We illustrate the efficacy of our proposed method using simulations, and two real applications, a neuroimaging dementia study and a digital advertising study.

stat.ML↗

arXiv2021

Generalized Connectivity Matrix Response Regression with Applications in Brain Connectivity Studies

Multiple-subject network data are fast emerging in recent years, where a separate connectivity matrix is measured over a common set of nodes for each individual subject, along with subject covariates information. In this article, we propose a new generalized matrix response regression model, where the observed networks are treated as matrix-valued responses and the subject covariates as predictors. The new model characterizes the population-level connectivity pattern through a low-rank intercept matrix, and the effect of subject covariates through a sparse slope tensor. We develop an efficient alternating gradient descent algorithm for parameter estimation, and establish the non-asymptotic error bound for the actual estimator from the algorithm, which quantifies the interplay between the computational and statistical errors. We further show the strong consistency for graph community recovery, as well as the edge selection consistency. We demonstrate the efficacy of our method through simulations and two brain connectivity studies.

stat.ME↗

arXiv2021

Sparse Tensor Additive Regression

Tensors are becoming prevalent in modern applications such as medical imaging and digital marketing. In this paper, we propose a sparse tensor additive regression (STAR) that models a scalar response as a flexible nonparametric function of tensor covariates. The proposed model effectively exploits the sparse and low-rank structures in the tensor additive regression. We formulate the parameter estimation as a non-convex optimization problem, and propose an efficient penalized alternating minimization algorithm. We establish a non-asymptotic error bound for the estimator obtained from each iteration of the proposed algorithm, which reveals an interplay between the optimization error and the statistical rate of convergence. We demonstrate the efficacy of STAR through extensive comparative simulation studies, and an application to the click-through-rate prediction in online advertising.

stat.ML↗

arXiv2021

Latent Network Structure Learning from High Dimensional Multivariate Point Processes

Learning the latent network structure from large scale multivariate point process data is an important task in a wide range of scientific and business applications. For instance, we might wish to estimate the neuronal functional connectivity network based on spiking times recorded from a collection of neurons. To characterize the complex processes underlying the observed data, we propose a new and flexible class of nonstationary Hawkes processes that allow both excitatory and inhibitory effects. We estimate the latent network structure using an efficient sparse least squares estimation approach. Using a thinning representation, we establish concentration inequalities for the first and second order statistics of the proposed Hawkes process. Such theoretical results enable us to establish the non-asymptotic error bound and the selection consistency of the estimated parameters. Furthermore, we describe a least squares loss based statistic for testing if the background intensity is constant in time. We demonstrate the efficacy of our proposed method through simulation studies and an application to a neuron spike train data set.

stat.ME↗

arXiv2020

Detection of the number of principal components by extended AIC-type method

Estimating the number of principal components is one of the fundamental problems in many scientific fields such as signal processing (or the spiked covariance model). In this paper, we first demonstrate that, for fixed $p$, any penalty term of the form $k'(p-k'/2+1/2)C_n$ may lead to an asymptotically consistent estimator under the condition that $C_n\to\infty$ and $C_n/n\to0$. We also extend our results to the case $n,p\to\infty$, with $p/n\to c>0$. In this case, for $k=o(n^{\frac{1}{3}})$, we first investigate the limiting laws for the leading eigenvalues of the sample covariance matrix $S_n$ under the condition that $λ_k>1+\sqrt{c}$. At low SNR, since the AIC tends to underestimate the number of signals $k$, the AIC should be re-defined in this case. As a natural extension of the AIC for fixed $p$, we propose the extended AIC (EAIC), i.e., the AIC-type method with tuning parameter $γ=φ(c)=1/2+\sqrt{1/c}-\log(1+\sqrt{c})/c$, and demonstrate that the EAIC-type method, i.e., the AIC-type method with tuning parameter $γ>φ(c)$, can select the number of signals $k$ consistently. In the following two cases, (1) $p$ fixed, $n\to\infty$, (2) $n,p\to\infty$ with $p/n\to 0$, if the AIC is defined as the degeneration of the EAIC in the case $n,p\to\infty$ with $p/n\to c>0$, i.e., $γ=\lim_{c\rightarrow 0+0}φ(c)=1$, then we have essentially demonstrated that, to achieve the consistency of the AIC-type method in the above two cases, $γ>1$ is required. Moreover, we show that the EAIC-type method is essentially tuning-free and outperforms the well-known KN estimator proposed in Kritchman and Nadler (2008) and the BCF estimator proposed in Bai, Choi and Fujikoshi (2018). Numerical studies indicate that the proposed method works well.

stat.ME↗

arXiv2019

Using Maximum Entry-Wise Deviation to Test the Goodness-of-Fit for Stochastic Block Models

The stochastic block model is widely used for detecting community structures in network data. How to test the goodness-of-fit of the model is one of the fundamental problems and has gained growing interests in recent years. In this article, we propose a novel goodness-of-fit test based on the maximum entry of the centered and re-scaled adjacency matrix for the stochastic block model. One noticeable advantage of the proposed test is that the number of communities can be allowed to grow linearly with the number of nodes ignoring a logarithmic factor. We prove that the null distribution of the test statistic converges in distribution to a Gumbel distribution, and we show that both the number of communities and the membership vector can be tested via the proposed method. Further, we show that the proposed test has asymptotic power guarantee against a class of alternatives. We also demonstrate that the proposed method can be extended to the degree-corrected stochastic block model. Both simulation studies and real-world data examples indicate that the proposed method works well.

stat.ME↗

arXiv2018

Mixed-Effect Time-Varying Network Model and Application in Brain Connectivity Analysis

Time-varying networks are fast emerging in a wide range of scientific and business disciplines. Most existing dynamic network models are limited to a single-subject and discrete-time setting. In this article, we propose a mixed-effect multi-subject continuous-time stochastic blockmodel that characterizes the time-varying behavior of the network at the population level, meanwhile taking into account individual subject variability. We develop a multi-step optimization procedure for a constrained stochastic blockmodel estimation, and derive the asymptotic property of the estimator. We demonstrate the effectiveness of our method through both simulations and an application to a study of brain development in youth.

stat.ME↗

arXiv2018

Modularity based community detection in heterogeneous networks

Heterogeneous networks are networks consisting of different types of nodes and multiple types of edges linking such nodes. While community detection has been extensively developed as a useful technique for analyzing networks that contain only one type of nodes, very few community detection techniques have been developed for heterogeneous networks. In this paper, we propose a modularity based community detection framework for heterogeneous networks. Unlike existing methods, the proposed approach has the flexibility to treat the number of communities as an unknown quantity. We describe a Louvain type maximization method for finding the community structure that maximizes the modularity function. Our simulation results show the advantages of the proposed method over existing methods. Moreover, the proposed modularity function is shown to be consistent under a heterogeneous stochastic blockmodel framework. Analyses of the DBLP four-area dataset and a MovieLens dataset demonstrate the usefulness of the proposed method.

cs.SI↗

arXiv2010

Sandage-Loeb test for the new agegraphic and Ricci dark energy models

The Sandage-Loeb (SL) test is a unique method to explore dark energy at the ``redshift desert'' ($2\lesssim z\lesssim 5$), an era not covered by any other dark energy probes, by directly measuring the temporal variation of the redshift of quasar (QSO) Lyman-$α$ absorption lines. In this paper, we study the prospects for constraining the new agegraphic dark energy (NADE) model and the Ricci dark energy (RDE) model with the SL test. We show that, assuming only a ten-year survey, the SL test can constrain these two models with high significance.

astro-ph.CO↗

arXiv2010

A more general interacting model of holographic dark energy

So far, there have been no theories or observational data that deny the presence of interaction between dark energy and dark matter. We extend naturally the holographic dark energy (HDE) model, proposed by Granda and Oliveros, in which the dark energy density includes not only the square of the Hubble scale, but also the time derivative of the Hubble scale to the case with interaction and the analytic forms for the cosmic parameters are obtained under the specific boundary conditions. The various behaviors concerning the cosmic expansion depend on the introduced numerical parameters which are also constrained. The more general interacting model inherits the features of the previous ones of HDE, keeping the consistency of the theory.

astro-ph.CO↗

arXiv2010

Theoretical Limits on Agegraphic Quintessence from Weak Gravity Conjecture

In this paper, we investigate the possible theoretical constraint on the parameter $n$ of the agegraphic quintessence model by considering the requirement of the weak gravity conjecture that the variation of the quintessence scalar field $ϕ$ should be less than the Planck mass $M_{\rm{p}}$. We obtain the theoretical upper bound $n\lesssim 2.5$ that is inconsistent with the current observational constraint result $2.637<n<2.983$ (95.4% CL). The possible implications of the tension between observational and theoretical constraint results are discussed.

gr-qc↗

arXiv2010

Interacting model of new agegraphic dark energy: Cosmological evolution and statefinder diagnostic

The statefinder diagnosic is a useful method for distinguishing different dark energy models. In this paper, we investigate the new agegraphic dark energy model with interaction between dark energy and matter component by using statefinder parameter pair $\{r, s\}$ and study its cosmological evolution. We plot the trajectories of the new agegraphic dark energy model for different interaction cases in the statefinder plane. As a result, the influence of the interaction on the evolution of the universe is shown in the statefinder diagrams.

astro-ph.CO↗

arXiv2009

Reconstructing quintom from WMAP 5-year observations: Generalized ghost condensate

In the 5-year WMAP data analysis, a new parametrization form for dark energy equation-of-state was used, and it has been shown that the equation-of-state, $w(z)$, crosses the cosmological-constant boundary $w=-1$. Based on this observation, in this paper, we investigate the reconstruction of quintom dark energy model. As a single-real-scalar-field model of dark energy, the generalized ghost condensate model provides us with a successful mechanism for realizing the quintom-like behavior. Therefore, we reconstruct this scalar-field quintom dark energy model from the WMAP 5-year observational results. As a comparison, we also discuss the quintom reconstruction based on other specific dark energy ansatzs, such as the CPL parametrization and the holographic dark energy scenarios.

astro-ph.CO↗