16 citations · 38 across the 10 of their papers we have counts for
11 papers
Approximating Smooth Functionals with ReLU Networks
Shuhao Jiao
We study the uniform approximation of smooth scalar-valued functionals on an infinite-dimensional separable Hilbert space by ReLU neural networks. A key feature in deep learning fo…
Filtration-Based Learning of Multiscale Shared Structures for Multiple Functional Predictors
Shuhao Jiao, Hernando Ombao, Ian W. McKeague
It is crucial to learn the shared structures among functional predictors, as these structures characterize how predictor components exert common effects and, more generally, how pr…
Coefficient Shape Transfer Learning for Functional Linear Regression
Shuhao Jiao, Ian W. Mckeague
The shapes of functions provide highly interpretable summaries of their trajectories. This article develops a novel transfer learning methodology to tackle the challenge of data sc…
Spectral Topological Data Analysis of Brain Signals
Anass El-Yaagoubi, Shuhao Jiao, Moo K. Chung +1
Topological analyses of brain functional connectivity usually reduce each pair of channels to a single scalar dependence, typically the Pearson correlation, and so cannot resolve t…
Coefficient Shape Alignment in Multiple Functional Linear Regression
Shuhao Jiao, Ngai-Hang Chan
In multivariate functional data analysis, different functional covariates often exhibit homogeneity. The covariates with pronounced homogeneity can be analyzed jointly within the s…
Filtrated Common Functional Principal Components for Multivariate Functional data
Shuhao Jiao, Ron D. Frostig, Hernando Ombao
Local field potentials (LFPs) are signals that measure electrical activity in localized cortical regions from implanted tetrodes in the human or animal brain. The LFP signals are c…
Change-point detection using spectral PCA for multivariate time series
Shuhao Jiao, Tong Shen, Zhaoxia Yu +1
We propose a two-stage approach Spec PC-CP to identify change points in multivariate time series. In the first stage, we obtain a low-dimensional summary of the high-dimensional ti…
Break Point Detection for Functional Covariance
Shuhao Jiao, Ron D. Frostig, Hernando Ombao
Many experiments record sequential trajectories where each trajectory consists of oscillations and fluctuations around zero. Such trajectories can be viewed as zero-mean functional…
Variation Pattern Classification of Functional Data
Shuhao Jiao, Ron D. Frostig, Hernando Ombao
A new classification method for functional data is proposed in this paper. This work is motivated by the need to identify features that discriminate between neurological conditions…
Shape-Preserving Prediction for Stationary Functional Time Series
Shuhao Jiao, Hernando Ombao
This article presents a novel method for prediction of stationary functional time series, in particular for trajectories that share a similar pattern but display variable phases. T…
Functional time series prediction under partial observation of the future curve
Shuhao Jiao, Alexander Aue, Hernando Ombao
This paper tackles one of the most fundamental goals in functional time series analysis which is to provide reliable predictions for future functions. Existing methods for predicti…