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Found 4 papers, 2 papers with code
On the Noise Sensitivity of the Randomized SVD
Lastly, we derive an optimal singular value shrinker for singular values and vectors obtained through the R-SVD, which may be useful for applications in matrix denoising.
On the Role of Channel Capacity in Learning Gaussian Mixture Models
While our results are only proved for GMMs whose centers are uniformly distributed over the sphere, they hint that perhaps it is the decoding error probability associated with the center constellation as a channel code that determines the statistical difficulty of learning the corresponding GMM, rather than just the minimum distance.
Spiked Covariance Estimation from Modulo-Reduced Measurements
Consider the rank-1 spiked model: $\bf{X}=\sqrt{\nu}\xi \bf{u}+ \bf{Z}$, where $\nu$ is the spike intensity, $\bf{u}\in\mathbb{S}^{k-1}$ is an unknown direction and $\xi\sim \mathcal{N}(0, 1),\bf{Z}\sim \mathcal{N}(\bf{0},\bf{I})$.
Multi-reference alignment in high dimensions: sample complexity and phase transition
Multi-reference alignment entails estimating a signal in $\mathbb{R}^L$ from its circularly-shifted and noisy copies.
