TreeV AE hierarchically divides samples according to their intrinsic characteristics, shedding light on hidden structures in the data.

Active learning is the task of using labelled data to select additional points to label, with the goal of fitting the most accurate model with a fixed budget of labelled points. In binary classification active learning is known to produce faster rates than passive learning for a broad range of settings.

Thismakesinferenceveryfast,andmaynot even need to use all input featuresto make a prediction (with axis-aligned trees).

We consider learning the structures of Gaussian latent tree models with vector observations when a subset of them are arbitrarily corrupted. First, we present the sample complexities of Recursive Grouping (RG)and Chow-Liu Recursive Grouping (CLRG)without theassumption thattheeffectivedepth isbounded in the number of observed nodes, significantly generalizing the results in Choi et al. (2011). We show that Chow-Liu initialization inCLRG greatly reduces the sample complexity ofRG from being exponential in the diameter of the tree to onlylogarithmic inthediameter forthehidden Markovmodel (HMM).