Search Results for author:
Found 21 papers, 5 papers with code
Generative Steganography Diffusion
However, existing GAN-based GS methods cannot completely recover the hidden secret data due to the lack of network invertibility, while Flow-based methods produce poor image quality due to the stringent reversibility restriction in each module.
Generative Steganography Network
A new steganographic approach called generative steganography (GS) has emerged recently, in which stego images (images containing secret data) are generated from secret data directly without cover media.
A non-graphical representation of conditional independence via the neighbourhood lattice
We introduce and study the neighbourhood lattice decomposition of a distribution, which is a compact, non-graphical representation of conditional independence that is valid in the absence of a faithful graphical representation.
Sequentially learning the topological ordering of causal directed acyclic graphs with likelihood ratio scores
We demonstrate a novel application of this general approach to estimate the topological ordering of a DAG.
Distributed Learning of Generalized Linear Causal Networks
We consider the task of learning causal structures from data stored on multiple machines, and propose a novel structure learning method called distributed annealing on regularized likelihood score (DARLS) to solve this problem.
Partitioned hybrid learning of Bayesian network structures
We develop a novel hybrid method for Bayesian network structure learning called partitioned hybrid greedy search (pHGS), composed of three distinct yet compatible new algorithms: Partitioned PC (pPC) accelerates skeleton learning via a divide-and-conquer strategy, $p$-value adjacency thresholding (PATH) effectively accomplishes parameter tuning with a single execution, and hybrid greedy initialization (HGI) maximally utilizes constraint-based information to obtain a high-scoring and well-performing initial graph for greedy search.
Perception des tons du mandarin par les apprenants fran\ccais : effets des contextes segmental et syllabique (In the present study, we report two experiments aimed at exploring the contributions of segmental and syllabic contexts to French learners' perception of Mandarin tones)
Dans la premi{\`e}re, des stimuli monosyllabiques produits naturellement, compos{\'e}s de 9 attaques ([{\o}(z{\'e}ro), p, t, tʰ, tɕ, ɕ, tʂ, tʂʰ, m]) et 2 rimes ([i, ɑu]), ont {\'e}t{\'e} identifi{\'e}s par 19 apprenants fran{\c{c}}ais de mandarin de niveau d{\'e}butant et 18 auditeurs de langue maternelle mandarin.
Causal network learning with non-invertible functional relationships
Discovery of causal relationships from observational data is an important problem in many areas.
Globally optimal score-based learning of directed acyclic graphs in high-dimensions
We prove that $\Omega(s\log p)$ samples suffice to learn a sparse Gaussian directed acyclic graph (DAG) from data, where $s$ is the maximum Markov blanket size.
On perfectness in Gaussian graphical models
Knowing when a graphical model is perfect to a distribution is essential in order to relate separation in the graph to conditional independence in the distribution, and this is particularly important when performing inference from data.
Learning Gaussian DAGs from Network Data
Structural learning of directed acyclic graphs (DAGs) or Bayesian networks has been studied extensively under the assumption that data are independent.
Optimizing regularized Cholesky score for order-based learning of Bayesian networks
We propose a novel structure learning method, annealing on regularized Cholesky score (ARCS), to search over topological sorts, or permutations of nodes, for a high-scoring Bayesian network.
Learning big Gaussian Bayesian networks: partition, estimation, and fusion
Structure learning of Bayesian networks has always been a challenging problem.
The neighborhood lattice for encoding partial correlations in a Hilbert space
We study the computational complexity of computing these structures and show that under a sparsity assumption, they can be computed in polynomial time, even in the absence of the assumption of perfectness to a graph.
Learning Large-Scale Bayesian Networks with the sparsebn Package
To meet this challenge, we have developed a new R package called sparsebn for learning the structure of large, sparse graphical models with a focus on Bayesian networks.
Learning Directed Acyclic Graphs with Penalized Neighbourhood Regression
We study a family of regularized score-based estimators for learning the structure of a directed acyclic graph (DAG) for a multivariate normal distribution from high-dimensional data with $p\gg n$.
Iterative Subsampling in Solution Path Clustering of Noisy Big Data
We develop an iterative subsampling approach to improve the computational efficiency of our previous work on solution path clustering (SPC).
Solution Path Clustering with Adaptive Concave Penalty
Fast accumulation of large amounts of complex data has created a need for more sophisticated statistical methodologies to discover interesting patterns and better extract information from these data.
Penalized Estimation of Directed Acyclic Graphs From Discrete Data
Bayesian networks, with structure given by a directed acyclic graph (DAG), are a popular class of graphical models.
Monte Carlo Simulation for Lasso-Type Problems by Estimator Augmentation
Regularized linear regression under the $\ell_1$ penalty, such as the Lasso, has been shown to be effective in variable selection and sparse modeling.
Concave Penalized Estimation of Sparse Gaussian Bayesian Networks
We develop a penalized likelihood estimation framework to estimate the structure of Gaussian Bayesian networks from observational data.
