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Found 10 papers, 3 papers with code
Fitting networks with a cancellation trick
The degree-corrected block model (DCBM), latent space model (LSM), and $\beta$-model are all popular network models.
Improved Algorithm and Bounds for Successive Projection
Vertex hunting is the problem of estimating the $K$ vertices of the simplex.
Recent Advances in Text Analysis
In particular, we propose a new statistical model for ranking the citation impacts of $11$ topics, and we also build a cross-topic citation graph to illustrate how research results on different topics spread to one another.
Subject clustering by IF-PCA and several recent methods
We also find that IF-PCA is quite competitive, which slightly outperforms Seurat and SC3 over the $8$ single-cell data sets.
Phase transition for detecting a small community in a large network
Using a degree-corrected block model (DCBM), we establish phase transitions of this testing problem concerning the size of the small community and the edge densities in small and large communities.
Sharp Impossibility Results for Hyper-graph Testing
In a broad Degree-Corrected Mixed-Membership (DCMM) setting, we test whether a non-uniform hypergraph has only one community or has multiple communities.
Improvements on SCORE, Especially for Weak Signals
It accommodates severe degree heterogeneity and is adaptive to different levels of sparsity, but its performance for networks with weak signals is unclear.
Phase Transitions for High Dimensional Clustering and Related Problems
In the two-dimensional phase space calibrating the rarity and strengths of useful features, we find the precise demarcation for the Region of Impossibility and Region of Possibility.
Optimal classification in sparse Gaussian graphic model
We propose a two-stage classification method where we first select features by the method of Innovated Thresholding (IT), and then use the retained features and Fisher's LDA for classification.
Optimality of Graphlet Screening in High Dimensional Variable Selection
Compared to m-variate brute-forth screening that has a computational cost of p^m, the GS only has a computational cost of p (up to some multi-log(p) factors) in screening.
