Search Results for author:
Found 7 papers, 1 papers with code
Sharp-SSL: Selective high-dimensional axis-aligned random projections for semi-supervised learning
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-aligned random projections of the data.
Automatic Change-Point Detection in Time Series via Deep Learning
We show how to automatically generate new offline detection methods based on training a neural network.
AIM 2020 Challenge on Rendering Realistic Bokeh
This paper reviews the second AIM realistic bokeh effect rendering challenge and provides the description of the proposed solutions and results.
High-dimensional, multiscale online changepoint detection
We introduce a new method for high-dimensional, online changepoint detection in settings where a $p$-variate Gaussian data stream may undergo a change in mean.
Sparse principal component analysis via axis-aligned random projections
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 the sample covariance matrix.
Average-case Hardness of RIP Certification
The restricted isometry property (RIP) for design matrices gives guarantees for optimal recovery in sparse linear models.
Statistical and computational trade-offs in estimation of sparse principal components
In this paper, we show that, under a widely-believed assumption from computational complexity theory, there is a fundamental trade-off between statistical and computational performance in this problem.
