6 citations · 7 across the 3 of their papers we have counts for
7 papers
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…
AIM 2020 Challenge on Rendering Realistic Bokeh
Andrey Ignatov, Radu Timofte, Ming Qian +32
This paper reviews the second AIM realistic bokeh effect rendering challenge and provides the description of the proposed solutions and results. The participating teams were solvin…
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…
Isotonic regression in general dimensions
Qiyang Han, Tengyao Wang, Sabyasachi Chatterjee +1
We study the least squares regression function estimator over the class of real-valued functions on that are increasing in each coordinate. For uniformly bounded signals…