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

December 9, 2023

CSL: Class-Agnostic Structure-Constrained Learning for Segmentation Including the Unseen
Hao Zhang, Fang Li, Lu Qi, Ming-Hsuan Yang, Narendra Ahuja
Segmentation Based Approach Structure Learning Zero Shot Semantic Segmentation Constrained Learning Distribution Segmentation

December 7, 2023

A Structural-Clustering Based Active Learning for Graph Neural Networks
Ricky Maulana Fajri, Yulong Pei, Lu Yin, Mykola Pechenizkiy
Graph Neural Network Active Learning Graph Structured Data Structure Learning PageRank Algorithm

November 17, 2023

Supervised structure learning
Karl J. Friston, Lancelot Da Costa, Alexander Tschantz, Alex Kiefer, Tommaso Salvatori, Victorita Neacsu, Magnus Koudahl, Conor Heins, Noor Sajid, Dimitrije Markovic, Thomas Parr, Tim Verbelen, Christopher L Buckley
Generative Model Graph Structure Learning Structure Learning Bayesian Model Selection Discrete Generative Model

November 6, 2023

Neural Structure Learning with Stochastic Differential Equations
Benjie Wang, Joel Jennings, Wenbo Gong
Variational Inference Stochastic Differential Equation Continuous Time Structure Learning Neural Stochastic Differential Equation Continuous Time Stochastic

November 1, 2023

Structure Learning with Adaptive Random Neighborhood Informed MCMC
Alberto Caron, Xitong Liang, Samuel Livingstone, Jim Griffin
Directed Acyclic Graph Structure Learning Efficient Sampling Approximate Sampling McMc Sampling Adaptive Metropolis Full Bayesian

October 17, 2023

Causal discovery using dynamically requested knowledge
Neville K Kitson, Anthony C Constantinou
Causal Discovery Knowledge Based Causal Relationship Structure Learning Causal Bayesian Network Graphical Structure Learning Structure

October 13, 2023

Fast & Efficient Learning of Bayesian Networks from Data: Knowledge Discovery and Causality
Minn Sein, Fu Shunkai
Raw Data Causal Relationship Causal Pattern Bayesian Network Efficient Learning Conditional Independence Structure Learning Knowledge Discovery Causal Network

September 15, 2023

Constraint-Free Structure Learning with Smooth Acyclic Orientations
Riccardo Massidda, Francesco Landolfi, Martina Cinquini, Davide Bacciu
Directed Acyclic Graph Structure Learning Adjacency Matrix Acyclic Graph

September 6, 2023

A Multimodal Learning Framework for Comprehensive 3D Mineral Prospectivity Modeling with Jointly Learned Structure-Fluid Relationships
Yang Zheng, Hao Deng, Ruisheng Wang, Jingjie Wu
Multimodal Learning Structure Learning Multimodal Fusion Model AU Dataset Mineral Prospectivity

September 5, 2023

June 24, 2023

Tuning structure learning algorithms with out-of-sample and resampling strategies
Kiattikun Chobtham, Anthony C. Constantinou
Practical Algorithm Inner Structure Hyperparameter Tuning Structure Learning Optimal Hyperparameters Auto Tuning Resampling Strategy

June 20, 2023

GraphGLOW: Universal and Generalizable Structure Learning for Graph Neural Networks
Wentao Zhao, Qitian Wu, Chenxiao Yang, Junchi Yan
Graph Neural Network Graph Structure Learning Universal Image Structure Learning Structural Generalization

May 22, 2023

Beneath Surface Similarity: Large Language Models Make Reasonable Scientific Analogies after Structure Abduction
Siyu Yuan, Jiangjie Chen, Xuyang Ge, Yanghua Xiao, Deqing Yang
Analogical Reasoning Structure Learning Emergent Reasoning Scientific Analogy Word Analogy

May 1, 2023

Variational Inference for Bayesian Neural Networks under Model and Parameter Uncertainty
Aliaksandr Hubin, Geir Storvik
Full Model Variational Inference Bayesian Neural Network Model Uncertainty Structure Learning Parameter Uncertainty Scalable Variational Scalable Bayesian

April 4, 2023

Structure Learning with Continuous Optimization: A Sober Look and Beyond
Ignavier Ng, Biwei Huang, Kun Zhang
Structure Learning Noise Estimation Continuous Optimization

March 17, 2023

SE-GSL: A General and Effective Graph Structure Learning Framework through Structural Entropy Optimization
Dongcheng Zou, Hao Peng, Xiang Huang, Renyu Yang, Jianxin Li, Jia Wu, Chunyang Liu, Philip S. Yu
Graph Neural Network Graph Structure Learning General Text Structure Learning Entropy Optimization Structural Entropy

February 23, 2023

Bayesian Structure Scores for Probabilistic Circuits
Yang Yang, Gennaro Gala, Robert Peharz
Expectation Maximization Probabilistic Graphical Model Structure Learning Probabilistic Circuit Bayesian Structure

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

Structure Learning

Papers

CSL: Class-Agnostic Structure-Constrained Learning for Segmentation Including the Unseen

A Structural-Clustering Based Active Learning for Graph Neural Networks

Supervised structure learning

Neural Structure Learning with Stochastic Differential Equations

Structure Learning with Adaptive Random Neighborhood Informed MCMC

Causal discovery using dynamically requested knowledge

Fast & Efficient Learning of Bayesian Networks from Data: Knowledge Discovery and Causality

Constraint-Free Structure Learning with Smooth Acyclic Orientations

A Multimodal Learning Framework for Comprehensive 3D Mineral Prospectivity Modeling with Jointly Learned Structure-Fluid Relationships

Structural Concept Learning via Graph Attention for Multi-Level Rearrangement Planning

RDGSL: Dynamic Graph Representation Learning with Structure Learning

Tuning structure learning algorithms with out-of-sample and resampling strategies

GraphGLOW: Universal and Generalizable Structure Learning for Graph Neural Networks

Beneath Surface Similarity: Large Language Models Make Reasonable Scientific Analogies after Structure Abduction

Variational Inference for Bayesian Neural Networks under Model and Parameter Uncertainty

Structure Learning with Continuous Optimization: A Sober Look and Beyond

SE-GSL: A General and Effective Graph Structure Learning Framework through Structural Entropy Optimization

Bayesian Structure Scores for Probabilistic Circuits

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

Directed Acyclic Graphs With Tears