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Found 14 papers, 3 papers with code
Fitting Low-rank Models on Egocentrically Sampled Partial Networks
This method is based on graph spectral properties and is computationally efficient for large-scale networks.
The non-overlapping statistical approximation to overlapping group lasso
Thanks to the separability, the computation of regularization based on our penalty is substantially faster than that of the overlapping group lasso, especially for large-scale and high-dimensional problems.
Incrementality Bidding via Reinforcement Learning under Mixed and Delayed Rewards
Incrementality, which is used to measure the causal effect of showing an ad to a potential customer (e. g. a user in an internet platform) versus not, is a central object for advertisers in online advertising platforms.
Diffusion Source Identification on Networks with Statistical Confidence
Diffusion source identification on networks is a problem of fundamental importance in a broad class of applications, including rumor controlling and virus identification.
Network Estimation by Mixing: Adaptivity and More
While many methods have been proposed to address this problem in recent years, they usually assume that the true model belongs to a known class, which is not verifiable in most real-world applications.
Informative core identification in complex networks
In network analysis, the core structure of modeling interest is usually hidden in a larger network in which most structures are not informative.
Community models for networks observed through edge nominations
We propose a general model for a class of network sampling mechanisms based on recording edges via querying nodes, designed to improve community detection for network data collected in this fashion.
Linear regression and its inference on noisy network-linked data
Linear regression on network-linked observations has been an essential tool in modeling the relationship between response and covariates with additional network structures.
Methodology
High-dimensional Gaussian graphical model for network-linked data
Graphical models are commonly used to represent conditional dependence relationships between variables.
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.
Probabilistic Rule Realization and Selection
Abstraction and realization are bilateral processes that are key in deriving intelligence and creativity.
Network cross-validation by edge sampling
While many statistical models and methods are now available for network analysis, resampling network data remains a challenging problem.
A note on the statistical view of matrix completion
A very simple interpretation of matrix completion problem is introduced based on statistical models.
High-dimensional Mixed Graphical Models
While graphical models for continuous data (Gaussian graphical models) and discrete data (Ising models) have been extensively studied, there is little work on graphical models linking both continuous and discrete variables (mixed data), which are common in many scientific applications.
