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Meta-Analysis of Randomized Experiments with Applications to Heavy-Tailed Response Data
The key insight of our procedure is that the noisy (but unbiased) difference-of-means estimate can be used as a ground truth ``label" on a portion of the RCT, to test the performance of an estimator trained on the other portion.
Improved graph Laplacian via geometric self-consistency
We address the problem of setting the kernel bandwidth used by Manifold Learning algorithms to construct the graph Laplacian.
Estimating Vector Fields on Manifolds and the Embedding of Directed Graphs
This paper considers the problem of embedding directed graphs in Euclidean space while retaining directional information.
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