Computing and Combinatorics Conference (Cocoon)
We study a problem arising in statistical analysis called the minimum bottleneck generalized matching problem that involves breaking up a population into blocks in order to carry out generalizable statistical analyses of randomized experiments. At a high level the problem is to find a clustering of the population such that each part is at least a given size and has at least a given number of elements from each treatment class (so that the experiments are statistically significant), and that all elements within a block are as similar as possible (to improve the accuracy of the analysis). (Read more)
Donglai Xiang, Timur Bagautdinov, Tuur Stuyck, Fabian Prada, Javier Romero, Weipeng Xu, Shunsuke Saito, Jingfan Guo, Breannan Smith, Takaaki Shiratori, Yaser Sheikh, Jessica Hodgins, Chenglei Wu
Ludwig Sidenmark, Mark Parent, Chi-Hao Wu, Joannes Chan, Michael Glueck, Daniel Wigdor, Tovi Grossman, Marcello Giordano
Xingyi Zhou, Rohit Girdhar, Armand Joulin, Philipp Krähenbühl, Ishan Misra
