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  1. We study the problem of selecting the largest among n unknown values x1,…,xn given only a single unbiased estimate yi for each xi. We design strategies that are simultaneously admissible (not uniformly dominated by any other strategy) and also never worse than a given baseline such as uniform random selection. We provide an application to stochastic optimization, specifically to the ubiquitous "iterate averaging" procedure in machine learning. 
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    Free, publicly-accessible full text available July 15, 2027
  2. We provide the first known analysis of the "Skip gram with negative sampling" procedure for DeepWalk embeddings of graphs drawn from the stochastic block model. 
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    Free, publicly-accessible full text available July 15, 2027
  3. We developed new algorithms for graph-augmentation in order to optimize the so-called Kirchoff Index of a graph. This is closely related to well-studied problems such as experiment design in statistics. 
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    Free, publicly-accessible full text available April 1, 2027