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In each competition, numerous practitioners repeatedly evaluated their progress against a holdout set that forms the basis of a public ranking availablethroughout the competition. Performance on a separate test set used only oncedetermined the final ranking.

Recent years have witnessed substantial progress in understanding the behavior of EM for mixture models that are correctly specified.

In particular, interpreting Random Forests (RFs) [2] and its variants [14, 28, 27, 29, 1, 12] has become an important area of research due to the wide ranging applications of RFs invarious scientific areas, such asgenome-wide association studies (GWAS)[7],gene expression microarray[13,23],andgeneregulatorynetworks[9].

Machine learning applied to protein sequences is an increasingly popular areaof research.

We give an algorithm which solves the problem to additiveaccuracy with O(1/) parallel depth and O n2/ work.

The coding rate can be accurately computed from finite samples of degenerate subspace-like distributions and can learn intrinsic representations in supervised, self-supervised, and unsupervised settings in a unified manner.

Furthermore, given random samples of pairwise comparisons, we show that a simple,"plug-in" estimator achieves(near-)optimal minimax samplecomplexity.

Furthermore, given random samples of pairwise comparisons, we show that a simple,"plug-in" estimator achieves(near-)optimal minimax samplecomplexity.