60 citations · 111 across the 14 of their papers we have counts for
11 papers · 1 filter
High-dimensional sparsity-adaptive multiple change-point detection
Hyeyoung Maeng, Tengyao Wang, Piotr Fryzlewicz
We introduce a method for detecting multiple change-points in the mean of a high-dimensional data sequence. Unlike existing top-down (i.e. divisive) algorithms, we adopt a bottom-u…
Deep learning with missing data
Tianyi Ma, Tengyao Wang, Richard J. Samworth
In the context of multivariate nonparametric regression with missing covariates, we propose Pattern Embedded Neural Networks (PENNs), which can be applied in conjunction with any e…
SpreadDetect: Detection of spreading change in a network over time
Hanqing Cai, Tengyao Wang
Change-point analysis has been successfully applied to the detect changes in multivariate data streams over time. In many applications, when data are observed over a graph/network,…
Sharp-SSL: Selective high-dimensional axis-aligned random projections for semi-supervised learning
Tengyao Wang, Edgar Dobriban, Milana Gataric +1
We propose a new method for high-dimensional semi-supervised learning problems based on the careful aggregation of the results of a low-dimensional procedure applied to many axis-a…
Inference in high-dimensional online changepoint detection
Yudong Chen, Tengyao Wang, Richard J. Samworth
We introduce and study two new inferential challenges associated with the sequential detection of change in a high-dimensional mean vector. First, we seek a confidence interval for…
High-dimensional changepoint estimation with heterogeneous missingness
Bertille Follain, Tengyao Wang, Richard J. Samworth
We propose a new method for changepoint estimation in partially-observed, high-dimensional time series that undergo a simultaneous change in mean in a sparse subset of coordinates.…
Estimation of high-dimensional change-points under a group sparsity structure
Hanqing Cai, Tengyao Wang
Change-points are a routine feature of 'big data' observed in the form of high-dimensional data streams. In many such data streams, the component series possess group structures an…
High-dimensional, multiscale online changepoint detection
Yudong Chen, Tengyao Wang, Richard J. Samworth
We introduce a new method for high-dimensional, online changepoint detection in settings where a -variate Gaussian data stream may undergo a change in mean. The procedure works…
High-dimensional principal component analysis with heterogeneous missingness
Ziwei Zhu, Tengyao Wang, Richard J. Samworth
We study the problem of high-dimensional Principal Component Analysis (PCA) with missing observations. In simple, homogeneous missingness settings with a noise level of constant or…
Sparse principal component analysis via axis-aligned random projections
Milana Gataric, Tengyao Wang, Richard J. Samworth
We introduce a new method for sparse principal component analysis, based on the aggregation of eigenvector information from carefully-selected axis-aligned random projections of th…
High-dimensional changepoint estimation via sparse projection
Tengyao Wang, Richard J. Samworth
Changepoints are a very common feature of Big Data that arrive in the form of a data stream. In this paper, we study high-dimensional time series in which, at certain time points,…