Wei Biao Wu
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View article: Statistical Guarantees for High-Dimensional Stochastic Gradient Descent
Statistical Guarantees for High-Dimensional Stochastic Gradient Descent Open
Stochastic Gradient Descent (SGD) and its Ruppert-Polyak averaged variant (ASGD) lie at the heart of modern large-scale learning, yet their theoretical properties in high-dimensional settings are rarely understood. In this paper, we provid…
View article: Comparative Analysis of Global and Local Probabilistic Time Series Forecasting for Contiguous Spatial Demand Regions
Comparative Analysis of Global and Local Probabilistic Time Series Forecasting for Contiguous Spatial Demand Regions Open
This study evaluates three probabilistic forecasting strategies using LightGBM: global pooling, cluster-level pooling, and station-level modeling across a range of scenarios, from fully homogeneous simulated data to highly heterogeneous re…
View article: Online Statistical Inference of Constrained Stochastic Optimization via Random Scaling
Online Statistical Inference of Constrained Stochastic Optimization via Random Scaling Open
Constrained stochastic nonlinear optimization problems have attracted significant attention for their ability to model complex real-world scenarios in physics, economics, and biology. As datasets continue to grow, online inference methods …
View article: Smoothed SGD for quantiles: Bahadur representation and Gaussian approximation
Smoothed SGD for quantiles: Bahadur representation and Gaussian approximation Open
This paper considers the estimation of quantiles via a smoothed version of the stochastic gradient descent (SGD) algorithm. By smoothing the score function in the conventional SGD quantile algorithm, we achieve monotonicity in the quantile…
View article: Online Inference for Quantiles by Constant Learning-Rate Stochastic Gradient Descent
Online Inference for Quantiles by Constant Learning-Rate Stochastic Gradient Descent Open
This paper proposes an online inference method of the stochastic gradient descent (SGD) with a constant learning rate for quantile loss functions with theoretical guarantees. Since the quantile loss function is neither smooth nor strongly …
View article: Change-point analysis with irregular signals
Change-point analysis with irregular signals Open
View article: Genome-wide identification and expression profile of HIR gene family members in Oryza sativa L
Genome-wide identification and expression profile of HIR gene family members in Oryza sativa L Open
The hypersensitive-induced reaction ( HIR ) gene family is associated with the hypersensitive response (HR) in plant defense against pathogens. Although rice ( Oryza sativa L.) is a crucial food crop, studies on its HIR genes are limited. …
View article: Change point analysis with irregular signals
Change point analysis with irregular signals Open
This paper considers the problem of testing and estimation of change point where signals after the change point can be highly irregular, which departs from the existing literature that assumes signals after the change point to be piece-wis…
View article: Asymptotics of Stochastic Gradient Descent with Dropout Regularization in Linear Models
Asymptotics of Stochastic Gradient Descent with Dropout Regularization in Linear Models Open
This paper proposes an asymptotic theory for online inference of the stochastic gradient descent (SGD) iterates with dropout regularization in linear regression. Specifically, we establish the geometric-moment contraction (GMC) for constan…
View article: Gaussian Approximation For Non-stationary Time Series with Optimal Rate and Explicit Construction
Gaussian Approximation For Non-stationary Time Series with Optimal Rate and Explicit Construction Open
Statistical inference for time series such as curve estimation for time-varying models or testing for existence of change-point have garnered significant attention. However, these works are generally restricted to the assumption of indepen…
View article: ℓ2 inference for change points in high-dimensional time series via a Two-Way MOSUM
ℓ2 inference for change points in high-dimensional time series via a Two-Way MOSUM Open
We propose an inference method for detecting multiple change points in high-dimensional time series, targeting dense or spatially clustered signals. Our method aggregates moving sum (MOSUM) statistics cross-sectionally by an ℓ2-…
View article: Time-varying multivariate causal processes
Time-varying multivariate causal processes Open
In this paper, we consider a wide class of time-varying multivariate causal processes that nests many classical and new examples as special cases. We first show the existence of a weakly dependent stationary approximation to initiate our t…
View article: High Confidence Level Inference is Almost Free using Parallel Stochastic Optimization
High Confidence Level Inference is Almost Free using Parallel Stochastic Optimization Open
Uncertainty quantification for estimation through stochastic optimization solutions in an online setting has gained popularity recently. This paper introduces a novel inference method focused on constructing confidence intervals with effic…
View article: Estimation of Grouped Time-Varying Network Vector Autoregression Models
Estimation of Grouped Time-Varying Network Vector Autoregression Models Open
View article: Local and Global: Temporal Question Answering via Information Fusion
Local and Global: Temporal Question Answering via Information Fusion Open
Many models that leverage knowledge graphs (KGs) have recently demonstrated remarkable success in question answering (QA) tasks. In the real world, many facts contained in KGs are time-constrained thus temporal KGQA has received increasing…
View article: Weighted Averaged Stochastic Gradient Descent: Asymptotic Normality and Optimality
Weighted Averaged Stochastic Gradient Descent: Asymptotic Normality and Optimality Open
Stochastic Gradient Descent (SGD) is one of the most popular algorithms in statistical and machine learning due to its computational and memory efficiency. Various averaging schemes have been proposed to accelerate the convergence of SGD i…
View article: Testing for parameter change epochs in GARCH time series
Testing for parameter change epochs in GARCH time series Open
Summary We develop a uniform test for detecting and dating the integrated or mildly explosive behaviour of a strictly stationary generalized autoregressive conditional heteroskedasticity (GARCH) process. Namely, we test the null hypothesis…
View article: High Dimensional Analysis of Variance in Multivariate Linear Regression
High Dimensional Analysis of Variance in Multivariate Linear Regression Open
In this paper, we develop a systematic theory for high dimensional analysis of variance in multivariate linear regression, where the dimension and the number of coefficients can both grow with the sample size. We propose a new \emph{U}~typ…
View article: Almost sure invariance principle of $β-$mixing time series in Hilbert space
Almost sure invariance principle of $β-$mixing time series in Hilbert space Open
Inspired by \citet{Berkes14} and \citet{Wu07}, we prove an almost sure invariance principle for stationary $β-$mixing stochastic processes defined on Hilbert space. Our result can be applied to Markov chain satisfying Meyn-Tweedie type Lya…
View article: $\ell^2$ Inference for Change Points in High-Dimensional Time Series via a Two-Way MOSUM
$\ell^2$ Inference for Change Points in High-Dimensional Time Series via a Two-Way MOSUM Open
We propose an inference method for detecting multiple change points in high-dimensional time series, targeting dense or spatially clustered signals. Our method aggregates moving sum (MOSUM) statistics cross-sectionally by an $\ell^2$-norm …
View article: Development of a Model Predicting the Outcome of In Vitro Fertilization Cycles by a Robust Decision Tree Method
Development of a Model Predicting the Outcome of In Vitro Fertilization Cycles by a Robust Decision Tree Method Open
Introduction Infertility is a worldwide problem. To evaluate the outcome of in vitro fertilization (IVF) treatment for infertility, many indicators need to be considered and the relation among indicators need to be studied. Objectives To c…
View article: Mask and Reason
Mask and Reason Open
Knowledge graph (KG) embeddings have been a mainstream approach for reasoning\nover incomplete KGs. However, limited by their inherently shallow and static\narchitectures, they can hardly deal with the rising focus on complex logical\nquer…
View article: Sequential Detection of Common Change in High-dimensional Data Stream
Sequential Detection of Common Change in High-dimensional Data Stream Open
After obtaining an accurate approximation for $ARL_0$, we first consider the optimal design of weight parameter for a multivariate EWMA chart that minimizes the stationary average delay detection time (SADDT). Comparisons with moving avera…
View article: Time-Varying Multivariate Causal Processes
Time-Varying Multivariate Causal Processes Open
In this paper, we consider a wide class of time-varying multivariate causal processes which nests many classic and new examples as special cases. We first prove the existence of a weakly dependent stationary approximation for our model whi…
View article: Time-Varying Multivariate Causal Processes
Time-Varying Multivariate Causal Processes Open
View article: Long‐term prediction intervals with many covariates
Long‐term prediction intervals with many covariates Open
Accurate forecasting is one of the fundamental focuses in the literature of econometric time‐series. Often practitioners and policymakers want to predict outcomes of an entire time horizon in the future instead of just a single k ‐step ahe…
View article: Popularity Bias Is Not Always Evil: Disentangling Benign and Harmful Bias for Recommendation
Popularity Bias Is Not Always Evil: Disentangling Benign and Harmful Bias for Recommendation Open
Recommender system usually suffers from severe popularity bias -- the collected interaction data usually exhibits quite imbalanced or even long-tailed distribution over items. Such skewed distribution may result from the users' conformity …
View article: Multiple regression on stable vectors
Multiple regression on stable vectors Open
We give necessary and sufficient conditions for the linearity of multiple regression on a general stable vector, along with a sufficient condition for the finiteness of the conditional absolute moment when 0 < α ≤ 1.
View article: Testing and estimation of clustered signals
Testing and estimation of clustered signals Open
We propose a change-point detection method for large scale multiple testing problems with data having clustered signals. Unlike the classic change-point setup, the signals can vary in size within a cluster. The clustering structure on the …
View article: Inference of Breakpoints in High-dimensional Time Series
Inference of Breakpoints in High-dimensional Time Series Open
For multiple change-points detection of high-dimensional time series, we provide asymptotic theory concerning the consistency and the asymptotic distribution of the breakpoint statistics and estimated break sizes. The theory backs up a sim…