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Found 9 papers, 5 papers with code
Any-dimensional equivariant neural networks
The fitted function is then defined on inputs of the same dimension.
Robust, randomized preconditioning for kernel ridge regression
This paper introduces two randomized preconditioning techniques for robustly solving kernel ridge regression (KRR) problems with a medium to large number of data points ($10^4 \leq N \leq 10^7$).
Stochastic Approximation with Decision-Dependent Distributions: Asymptotic Normality and Optimality
We show that under mild assumptions, the deviation between the average iterate of the algorithm and the solution is asymptotically normal, with a covariance that clearly decouples the effects of the gradient noise and the distributional shift.
Clustering a Mixture of Gaussians with Unknown Covariance
We investigate a clustering problem with data from a mixture of Gaussians that share a common but unknown, and potentially ill-conditioned, covariance matrix.
Escaping strict saddle points of the Moreau envelope in nonsmooth optimization
The main conclusion is that a variety of algorithms for nonsmooth optimization can escape strict saddle points of the Moreau envelope at a controlled rate.
Efficient Clustering for Stretched Mixtures: Landscape and Optimality
This paper considers a canonical clustering problem where one receives unlabeled samples drawn from a balanced mixture of two elliptical distributions and aims for a classifier to estimate the labels.
Low-rank matrix recovery with composite optimization: good conditioning and rapid convergence
The task of recovering a low-rank matrix from its noisy linear measurements plays a central role in computational science.
Composite optimization for robust blind deconvolution
The blind deconvolution problem seeks to recover a pair of vectors from a set of rank one bilinear measurements.
Local angles and dimension estimation from data on manifolds
For data living in a manifold $M\subseteq \mathbb{R}^m$ and a point $p\in M$ we consider a statistic $U_{k, n}$ which estimates the variance of the angle between pairs of vectors $X_i-p$ and $X_j-p$, for data points $X_i$, $X_j$, near $p$, and evaluate this statistic as a tool for estimation of the intrinsic dimension of $M$ at $p$.
