Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Provable Tensor Methods for Learning Mixtures of Generalized Linear
  Models   We consider the problem of learning mixtures of generalized linear models
(GLM) which arise in classification and regression problems. Typical learning
approaches such as expectation maximization (EM) or variational Bayes can get
stuck in spurious local optima. In contrast, we present a tensor decomposition
method which is guaranteed to correctly recover the parameters. The key insight
is to employ certain feature transformations of the input, which depend on the
input generative model. Specifically, we employ score function tensors of the
input and compute their cross-correlation with the response variable. We
establish that the decomposition of this tensor consistently recovers the
parameters, under mild non-degeneracy conditions. We demonstrate that the
computational and sample complexity of our method is a low order polynomial of
the input and the latent dimensions.

22
The categories of this paper are: chao-dyn
7
The categories of this paper are: hep-th
29
The categories of this paper are: funct-an
11
The categories of this paper are: hep-lat
23
The categories of this paper are: solv-int
17
The categories of this paper are: q-fin
9
The categories of this paper are: nlin
14
The categories of this paper are: math-ph
13
The categories of this paper are: quant-ph
8
The categories of this paper are: hep-ex
19
The categories of this paper are: eess
32
The categories of this paper are: atom-ph
24
The categories of this paper are: dg-ga
34
The categories of this paper are: chem-ph
31
The categories of this paper are: ao-sci
10
The categories of this paper are: q-bio