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Found 6 papers, 2 papers with code
Statistical Inference with Limited Memory: A Survey
The problem of statistical inference in its various forms has been the subject of decades-long extensive research.
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 $\xi\sim \mathcal{N}(0, 1),\bf{Z}\sim \mathcal{N}(\bf{0},\bf{I})$.
Blind Modulo Analog-to-Digital Conversion
In a growing number of applications, there is a need to digitize signals whose spectral characteristics are challenging for traditional Analog-to-Digital Converters (ADCs).
Constructing Multiclass Classifiers using Binary Classifiers Under Log-Loss
For this method we prove that the multiclass regret is exactly a weighted sum of constituent binary regrets where the weighing is determined by the tree structure.
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.
