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Found 11 papers, 2 papers with code
Optimal estimation of Gaussian (poly)trees
We develop optimal algorithms for learning undirected Gaussian trees and directed Gaussian polytrees from data.
Inconsistency of cross-validation for structure learning in Gaussian graphical models
In this paper, we highlight the inherent limitations of cross-validation when employed to discern the structure of a Gaussian graphical model.
On Mergable Coresets for Polytope Distance
We show that a constant-size constant-error coreset for polytope distance is simple to maintain under merges of coresets.
Learning Mixtures of Gaussians with Censored Data
We study the problem of learning mixtures of Gaussians with censored data.
Tight Bounds on the Hardness of Learning Simple Nonparametric Mixtures
Combining these bounds, we conclude that the optimal sample complexity of this problem properly lies in between polynomial and exponential, which is not common in learning theory.
Optimal estimation of Gaussian DAG models
In other words, at least for Gaussian models with equal error variances, learning a directed graphical model is statistically no more difficult than learning an undirected graphical model.
Optimal Coreset for Gaussian Kernel Density Estimation
We study how to construct a small subset $Q$ of $P$ such that the kernel density estimate of $P$ is approximated by the kernel density estimate of $Q$.
Approximate Guarantees for Dictionary Learning
The problem is formalized as factorizing a matrix $X (d \times n)$ (whose columns are the signals) as $X = AY$, where $A$ has a prescribed number $m$ of columns (typically $m \ll n$), and $Y$ has columns that are $k$-sparse (typically $k \ll d$).
The GaussianSketch for Almost Relative Error Kernel Distance
We introduce two versions of a new sketch for approximately embedding the Gaussian kernel into Euclidean inner product space.
Near-Optimal Coresets of Kernel Density Estimates
When $d\geq 1/\varepsilon^2$, it is known that the size of coreset can be $O(1/\varepsilon^2)$.
Improved Coresets for Kernel Density Estimates
When the dimension $d$ is constant, we demonstrate much tighter bounds on the size of the coreset specifically for Gaussian kernels, showing that it is bounded by the size of the coreset for axis-aligned rectangles.
