Supplementary Material for the AISTATS 2016 Paper: Provable Tensor Methods for Learning Mixtures of Generalized Linear Models

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A Kernelized Stein Discrepancy for Goodness-of-fit Tests (2016, background): “Theorem 4.1 suggests that n ˆ S u ( p, q ) has a well deﬁned limit distribution under the null p = q , that is, n ˆ S u ( p, q ) ∞ with probability one, but grows to ∞ at a √ n -rate under any ﬁxed alternative hypothesis q (cid:54) = p .”
Action-depedent Control Variates for Policy Optimization via Stein's Identity (2017, unknown): “An incomplete list includes Gorham & Mackey (2015), Oates et al. (2017), Oates et al. (2016), Chwialkowski et al. (2016), Liu et al. (2016), Sedghi et al. (2016), Liu & Wang (2016), Feng et al. (2017), Liu & Lee (2017).”
Polynomial Convergence of Gradient Descent for Training One-Hidden-Layer Neural Networks (2018, background): “Another line of work investigates classes of such networks that can be learned in polynomial time, notably using tensor methods [17, 22] and polynomial kernels [12, 13] and a combination of tensor initialization followed by gradient descent [26].”