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Found 15 papers, 0 papers with code
Optimal and Private Learning from Human Response Data
Firstly, we obtain a refined entrywise error bound for the spectral algorithm, complementing the `average error' $\ell_2$ bound in their work.
Efficient and Accurate Learning of Mixtures of Plackett-Luce Models
We propose an initialization algorithm that can provide a provably accurate initial estimate and an EM algorithm that maximizes the true log-likelihood function efficiently.
Leave-one-out Singular Subspace Perturbation Analysis for Spectral Clustering
The singular subspaces perturbation theory is of fundamental importance in probability and statistics.
Uncertainty quantification in the Bradley-Terry-Luce model
The Bradley-Terry-Luce (BTL) model is a benchmark model for pairwise comparisons between individuals.
Optimal Orthogonal Group Synchronization and Rotation Group Synchronization
We study the statistical estimation problem of orthogonal group synchronization and rotation group synchronization.
Optimal Full Ranking from Pairwise Comparisons
We consider the problem of ranking $n$ players from partial pairwise comparison data under the Bradley-Terry-Luce model.
Optimal Clustering in Anisotropic Gaussian Mixture Models
We study the clustering task under anisotropic Gaussian Mixture Models where the covariance matrices from different clusters are unknown and are not necessarily the identical matrix.
SDP Achieves Exact Minimax Optimality in Phase Synchronization
We study the phase synchronization problem with noisy measurements $Y=z^*z^{*H}+\sigma W\in\mathbb{C}^{n\times n}$, where $z^*$ is an $n$-dimensional complex unit-modulus vector and $W$ is a complex-valued Gaussian random matrix.
Partial Recovery for Top-$k$ Ranking: Optimality of MLE and Sub-Optimality of Spectral Method
We also derive the optimal signal to noise ratio condition for the exact recovery of the top-$k$ set.
Iterative Algorithm for Discrete Structure Recovery
We propose a general modeling and algorithmic framework for discrete structure recovery that can be applied to a wide range of problems.
Optimality of Spectral Clustering in the Gaussian Mixture Model
Spectral clustering is one of the most popular algorithms to group high dimensional data.
Bayesian Time Series Forecasting with Change Point and Anomaly Detection
However, their performance is easily undermined by the existence of change points and anomaly points, two structures commonly observed in real data, but rarely considered in the aforementioned methods.
Theoretical and Computational Guarantees of Mean Field Variational Inference for Community Detection
Its iterative Coordinate Ascent Variational Inference algorithm has been widely applied to large scale Bayesian inference.
Community Detection in Degree-Corrected Block Models
Community detection is a central problem of network data analysis.
Achieving Optimal Misclassification Proportion in Stochastic Block Model
Community detection is a fundamental statistical problem in network data analysis.
