Search Results for author: Qing Zhou

Found 20 papers, 5 papers with code

Generative Steganography Network

no code implementations28 Jul 2022 Ping Wei, Sheng Li, Xinpeng Zhang, Ge Luo, Zhenxing Qian, Qing Zhou

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.

Image Generation

A non-graphical representation of conditional independence via the neighbourhood lattice

no code implementations12 Jun 2022 Arash A. Amini, Bryon Aragam, Qing Zhou

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.

Distributed Learning of Generalized Linear Causal Networks

no code implementations23 Jan 2022 Qiaoling Ye, Arash A. Amini, Qing Zhou

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.

Distributed Optimization

Partitioned hybrid learning of Bayesian network structures

no code implementations22 Mar 2021 Jireh Huang, Qing Zhou

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)

no code implementations JEPTALNRECITAL 2020 Qing Zhou, Didier Demolin

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

no code implementations20 Apr 2020 Bingling Wang, Qing Zhou

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

no code implementations NeurIPS 2019 Bryon Aragam, Arash Amini, Qing Zhou

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

no code implementations3 Sep 2019 Arash A. Amini, Bryon Aragam, Qing Zhou

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

no code implementations26 May 2019 Hangjian Li, Oscar Hernan Madrid Padilla, Qing Zhou

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

1 code implementation28 Apr 2019 Qiaoling Ye, Arash A. Amini, Qing Zhou

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

no code implementations24 Apr 2019 Jiaying Gu, Qing Zhou

Structure learning of Bayesian networks has always been a challenging problem.

The neighborhood lattice for encoding partial correlations in a Hilbert space

1 code implementation3 Nov 2017 Arash A. Amini, Bryon Aragam, Qing Zhou

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.

Dimensionality Reduction

Learning Large-Scale Bayesian Networks with the sparsebn Package

3 code implementations11 Mar 2017 Bryon Aragam, Jiaying Gu, Qing Zhou

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

2 code implementations29 Nov 2015 Bryon Aragam, Arash A. Amini, Qing Zhou

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

no code implementations4 Dec 2014 Yuliya Marchetti, Qing Zhou

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

no code implementations24 Apr 2014 Yuliya Marchetti, Qing Zhou

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

2 code implementations10 Mar 2014 Jiaying Gu, Fei Fu, Qing Zhou

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

no code implementations17 Jan 2014 Qing Zhou

Regularized linear regression under the $\ell_1$ penalty, such as the Lasso, has been shown to be effective in variable selection and sparse modeling.

Variable Selection

Concave Penalized Estimation of Sparse Gaussian Bayesian Networks

no code implementations4 Jan 2014 Bryon Aragam, Qing Zhou

We develop a penalized likelihood estimation framework to estimate the structure of Gaussian Bayesian networks from observational data.

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