We prove new explicit upper bounds on the leverage scores of Fourier sparse functions underboththeGaussian andLaplacemeasures.

We study linear and kernel classification in the streaming model. For linear classification, we improve upon the algorithm of (Tai, et al. 2018), which solves the $\ell_1$ point query problem on the optimal weight vector $w_* \in \mathbb{R}^d$ in sublinear space. We first give an algorithm solving the more difficult $\ell_2$ point query problem on $w_*$, also in sublinear space. We also give an algorithm which solves the $\ell_2$ heavy hitter problem on $w_*$, in sublinear space and running time. Finally, we give an algorithm which can $\textit{deterministically}$ solve the $\ell_1$ point query problem on $w_*$, with sublinear space improving upon that of (Tai, et al. 2018). For kernel classification, if $w_* \in \mathbb{R}^{d^p}$ is the optimal weight vector classifying points in the stream according to their $p^{th}$-degree polynomial kernel, then we give an algorithm solving the $\ell_2$ point query problem on $w_*$ in $\text{poly}(\frac{p \log d}{\varepsilon})$ space, and an algorithm solving the $\ell_2$ heavy hitter problem in $\text{poly}(\frac{p \log d}{\varepsilon})$ space and running time. Note that our space and running time are polynomial in $p$, making our algorithms well-suited to high-degree polynomial kernels and the Gaussian kernel (approximated by the polynomial kernel of degree $p = \Theta(\log T)$).

Low-rank approximation is a common tool used to accelerate kernel methods: the n n kernel matrix K is approximated via a rank-k matrix K which can be stored in much less space and processed more quickly. In this work we study the limits of computationally efficient low-rank kernel approximation.

We introduce a nonparametric approach for estimating drift functions in systems of stochastic differential equations from sparse observat ions of the state vector. Using a Gaussian process prior over the drift as a function of the state vector, we develop an approximate EM algorithm to deal with the unobser ved, latent dynamics between observations. The posterior over states is appr oximated by a piecewise linearized process of the Ornstein-Uhlenbeck type and the M AP estimation of the drift is facilitated by a sparse Gaussian process regressio n.

K, when n is large (e.g. The time complexity is O ( kτn). We have the following result. It shows that when two data points in X, e.g. Hence KLSR with Gaussian kernel utilizes local information to enhance C . The algorithm of AutoSC-GD with only LSR and KLSR is shown in Algorithm 1.

We introduce a nonparametric approach for estimating drift functions in systems of stochastic differential equations from sparse observat ions of the state vector. Using a Gaussian process prior over the drift as a function of the state vector, we develop an approximate EM algorithm to deal with the unobser ved, latent dynamics between observations. The posterior over states is appr oximated by a piecewise linearized process of the Ornstein-Uhlenbeck type and the M AP estimation of the drift is facilitated by a sparse Gaussian process regressio n.