Structure Learning

Structure learning focuses on automatically discovering the underlying relationships between variables in data, often represented as graphs, to build probabilistic models. Current research emphasizes developing robust and scalable algorithms, including those based on mutual information, optimal transport, and graph neural networks, to address challenges like handling missing data, nonlinearity, and high dimensionality. These advancements are crucial for improving the accuracy and efficiency of causal discovery, enabling better understanding of complex systems in diverse fields such as drug discovery, security, and image-text matching. Furthermore, integrating human knowledge and addressing the limitations of cross-validation are active areas of investigation.

Papers

February 9, 2023

Principled and Efficient Motif Finding for Structure Learning of Lifted Graphical Models
Jonathan Feldstein, Dominic Phillips, Efthymia Tsamoura
General Principle Structure Learning Motif Discovery Statistical Relational Lifted Inference Sequence Motif

February 4, 2023

Directed Acyclic Graphs With Tears
Zhichao Chen, Zhiqiang Ge
Gradient Descent Bayesian Network Directed Acyclic Graph Structure Learning Discrete Optimization Tear Film

January 30, 2023

Structure Learning and Parameter Estimation for Graphical Models via Penalized Maximum Likelihood Methods
Maryia Shpak
Bayesian Network Parameter Estimation Probabilistic Graphical Model Graphical Model Structure Learning Generalized Linear Model Lasso Estimator

January 26, 2023

Causal Structural Learning from Time Series: A Convex Optimization Approach
Song Wei, Yao Xie
Time Series Convex Optimization Structure Learning Causal Structure Learning Adaptive Linear

August 22, 2022

August 14, 2022

Novel Ordering-based Approaches for Causal Structure Learning in the Presence of Unobserved Variables
Ehsan Mokhtarian, Mohammadsadegh Khorasani, Jalal Etesami, Negar Kiyavash
Constructive Approach Speech Presence Long Form Novel Structure Learning Causal Structure Learning Unobserved Variable Markov Equivalence Class Causal Order Ancestral Graph

August 11, 2022

Structural Biases for Improving Transformers on Translation into Morphologically Rich Languages
Paul Soulos, Sudha Rao, Caitlin Smith, Eric Rosen, Asli Celikyilmaz, R. Thomas McCoy, Yichen Jiang, Coleman Haley, Roland Fernandez, Hamid Palangi, Jianfeng Gao, Paul Smolensky
Transformer Megatron Decepticons Machine Translation Transformer Based Model View Translation Structure Learning Language Structure Morphological Tokenization Structural Bias Morphologically Rich Language

June 14, 2022

Highly Efficient Structural Learning of Sparse Staged Trees
Manuele Leonelli, Gherardo Varando
Many Sparse Bayesian Network Tree Specie Structure Learning Bidirectional Correspondence Tree Model

May 25, 2022

Amortized Inference for Causal Structure Learning
Lars Lorch, Scott Sussex, Jonas Rothfuss, Andreas Krause, Bernhard Schölkopf
Variational Inference Causal Discovery Causal Structure Structure Learning Causal Structure Learning Amortized Inference

May 24, 2022

Graph Neural Networks Intersect Probabilistic Graphical Models: A Survey
Chenqing Hua, Sitao Luan, Qian Zhang, Jie Fu
Graph Neural Network Timely Survey Graph Structured Data Probabilistic Graphical Model Structured Representation Structure Learning

May 6, 2022

Structure Learning in Graphical Models from Indirect Observations
Hang Zhang, Afshin Abdi, Faramarz Fekri
Graphical Model Structure Learning Dimensional Random Vector Graphical Structure Markov Blanket

April 29, 2022

Tractable Uncertainty for Structure Learning
Benjie Wang, Matthew Wicker, Marta Kwiatkowska
Structure Learning Bayesian Structure Posterior Uncertainty

March 8, 2022

Structural Learning of Simple Staged Trees
Manuele Leonelli, Gherardo Varando
Bayesian Network Tree Specie Conditional Independence Structure Learning Random Vector

January 27, 2022

Constrained Structure Learning for Scene Graph Generation
Daqi Liu, Miroslaw Bober, Josef Kittler
Variational Inference Scene Graph Generation Structure Learning Variational Bayesian Variational Objective

December 20, 2021

December 16, 2021

December 9, 2021

Fair Community Detection and Structure Learning in Heterogeneous Graphical Models
Davoud Ataee Tarzanagh, Laura Balzano, Alfred O. Hero
Community Detection Probabilistic Graphical Model Structure Learning Pseudo Likelihood Gaussian Graphical Model

Structure Learning

Papers

Principled and Efficient Motif Finding for Structure Learning of Lifted Graphical Models

Directed Acyclic Graphs With Tears

Structure Learning and Parameter Estimation for Graphical Models via Penalized Maximum Likelihood Methods

Causal Structural Learning from Time Series: A Convex Optimization Approach

BigBraveBN: algorithm of structural learning for bayesian networks with a large number of nodes

Low-Level Physiological Implications of End-to-End Learning of Speech Recognition

Novel Ordering-based Approaches for Causal Structure Learning in the Presence of Unobserved Variables

Structural Biases for Improving Transformers on Translation into Morphologically Rich Languages

Highly Efficient Structural Learning of Sparse Staged Trees

Amortized Inference for Causal Structure Learning

Graph Neural Networks Intersect Probabilistic Graphical Models: A Survey

Structure Learning in Graphical Models from Indirect Observations

Tractable Uncertainty for Structure Learning

Structural Learning of Simple Staged Trees

Constrained Structure Learning for Scene Graph Generation

Encoding Hierarchical Information in Neural Networks helps in Subpopulation Shift

Hybrid Bayesian network discovery with latent variables by scoring multiple interventions

The Dual PC Algorithm and the Role of Gaussianity for Structure Learning of Bayesian Networks

BoGraph: Structured Bayesian Optimization From Logs for Expensive Systems with Many Parameters

Fair Community Detection and Structure Learning in Heterogeneous Graphical Models