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Found 13 papers, 2 papers with code
Learning from learning machines: a new generation of AI technology to meet the needs of science
We outline emerging opportunities and challenges to enhance the utility of AI for scientific discovery.
Sparse Canonical Correlation Analysis via Concave Minimization
We also introduceDirected Sparse CCA, which is able to find associations which are aligned with a specified experiment design, andMulti-View sCCA which is used to discover associations between multiple sets of covariates.
Hierarchical community detection by recursive partitioning
This can be done with a simple top-down recursive partitioning algorithm, starting with a single community and separating the nodes into two communities by spectral clustering repeatedly, until a stopping rule suggests there are no further communities.
Two provably consistent divide and conquer clustering algorithms for large networks
In this article, we advance divide-and-conquer strategies for solving the community detection problem in networks.
Network modelling of topological domains using Hi-C data
However, one of the drawbacks of community detection is that most methods take exchangeability of the nodes in the network for granted; whereas the nodes in this case, i. e. the positions on the chromosomes, are not exchangeable.
Applications Genomics
Meta-learners for Estimating Heterogeneous Treatment Effects using Machine Learning
There is growing interest in estimating and analyzing heterogeneous treatment effects in experimental and observational studies.
Statistics Theory Methodology Statistics Theory
The Consistency of Common Neighbors for Link Prediction in Stochastic Blockmodels
Link prediction and clustering are key problems for network-structureddata.
Spectral Clustering and Block Models: A Review And A New Algorithm
We focus on spectral clustering of unlabeled graphs and review some results on clustering methods which achieve weak or strong consistent identification in data generated by such models.
Community Detection in Networks using Graph Distance
There are some exceptions but all have some incomplete theoretical basis.
Hypothesis Testing for Automated Community Detection in Networks
Community detection in networks is a key exploratory tool with applications in a diverse set of areas, ranging from finding communities in social and biological networks to identifying link farms in the World Wide Web.
Role of normalization in spectral clustering for stochastic blockmodels
The quality of spectral clustering is closely tied to the convergence properties of these principal eigenvectors.
Pseudo-likelihood methods for community detection in large sparse networks
Many algorithms have been proposed for fitting network models with communities, but most of them do not scale well to large networks, and often fail on sparse networks.
The method of moments and degree distributions for network models
Probability models on graphs are becoming increasingly important in many applications, but statistical tools for fitting such models are not yet well developed.
Statistics Theory Statistics Theory
