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Lag selection and estimation of stable parameters for multiple autoregressive processes through convex programming
We prove that the estimated process is stable, and we establish rates for the forecasting error that can outmatch the known rate in our setting.
Learning Mixtures of Spherical Gaussians via Fourier Analysis
Suppose that we are given independent, identically distributed samples $x_l$ from a mixture $\mu$ of no more than $k$ of $d$-dimensional spherical gaussian distributions $\mu_i$ with variance $1$, such that the minimum $\ell_2$ distance between two distinct centers $y_l$ and $y_j$ is greater than $\sqrt{d} \Delta$ for some $c \leq \Delta $, where $c\in (0, 1)$ is a small positive universal constant.
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