Probabilistic Dependency

Probabilistic dependency modeling focuses on representing and reasoning with uncertainties in relationships between variables, aiming to build accurate and interpretable models of complex systems. Current research emphasizes developing advanced probabilistic graphical models, including Bayesian networks, graph neural networks, and novel autoencoder architectures, to capture intricate dependencies and perform efficient inference. These advancements are improving predictions in diverse fields like engineering design, natural language processing, and spatiotemporal analysis, while also enhancing our understanding of uncertainty quantification and causal inference. The resulting models offer improved decision-making capabilities in resource-constrained environments and provide more robust predictions in the face of inherent randomness.

Papers

September 18, 2024

PieClam: A Universal Graph Autoencoder Based on Overlapping Inclusive and Exclusive Communities
Daniel Zilberg, Ron Levie
Community Structure Graph AutoEncoder Graph Autoencoders Small Community Probabilistic Dependency Cluster Specific Random Effect Graph Similarity Computation

August 6, 2024

Sequential Conditional Transport on Probabilistic Graphs for Interpretable Counterfactual Fairness
Agathe Fernandes Machado, Arthur Charpentier, Ewen Gallic
Optimal Transport Causal Graph High Quality Counterfactuals Graphical Model Counterfactual Fairness Counterfactual Method Probabilistic Dependency Conditional Transport

June 4, 2024

Bayesian Mesh Optimization for Graph Neural Networks to Enhance Engineering Performance Prediction
Jangseop Park, Namwoo Kang
Graph Neural Network Surrogate Model Performance Prediction Probabilistic Dependency Deep Learning Based Surrogate Linear Surrogate

March 17, 2024

Circular Belief Propagation for Approximate Probabilistic Inference
Vincent Bouttier, Renaud Jardri, Sophie Deneve
Belief Propagation Probabilistic Dependency Cognitive Augmentation Probabilistic Inference Algorithm

November 26, 2023

Probabilistic Transformer: A Probabilistic Dependency Model for Contextual Word Representation
Haoyi Wu, Kewei Tu
Contextual Representation Word Representation Contextual Embeddings Syntactic Structure Probabilistic Dependency Probabilistic Transformer

November 9, 2023

Inference for Probabilistic Dependency Graphs
Oliver E. Richardson, Joseph Y. Halpern, Christopher De Sa
Scientific Inference Probabilistic Graphical Model Graphical Model Probabilistic Dependency

September 16, 2023

Semantic Information Extraction for Text Data with Probability Graph
Zhouxiang Zhao, Zhaohui Yang, Ye Hu, Licheng Lin, Zhaoyang Zhang
Semantic Information Text Data Probabilistic Dependency Semantic Extraction Text Transmission

March 7, 2023

Uncertainty Quantification of Spatiotemporal Travel Demand with Probabilistic Graph Neural Networks
Qingyi Wang, Shenhao Wang, Dingyi Zhuang, Haris Koutsopoulos, Jinhua Zhao
Graph Neural Network Uncertainty Quantification Travel Demand Probabilistic Dependency Historical Ridership Data Spatiotemporal Travel Demand

November 15, 2022

Hierarchical Inference of the Lensing Convergence from Photometric Catalogs with Bayesian Graph Neural Networks
Ji Won Park, Simon Birrer, Madison Ueland, Miles Cranmer, Adriano Agnello, Sebastian Wagner-Carena, Philip J. Marshall, Aaron Roodman, the LSST Dark Energy Science Collaboration
Graph Neural Network Bayesian Posterior Probabilistic Dependency Hierarchical Bayesian Model Hierarchical Inference Mass Convergence Event Export Import Exchange Rate Convergence

September 2, 2022

Inference and dynamic decision-making for deteriorating systems with probabilistic dependencies through Bayesian networks and deep reinforcement learning
Pablo G. Morato, Charalampos P. Andriotis, Konstantinos G. Papakonstantinou, Philippe Rigo
Deep Reinforcement Learning Scientific Inference Policy OpTimization Bayesian Network Global Optimization Observable Markov Decision Process Reinforcement Learning Approach Dynamic Decision Probabilistic Dependency Optimal Execution

August 9, 2022

Causal Effect Identification in Uncertain Causal Networks
Sina Akbari, Fateme Jamshidi, Ehsan Mokhtarian, Matthew J. Vowels, Jalal Etesami, Negar Kiyavash
Causal Graph Causal Structure Causal Query Causal Effect Identification Probabilistic Dependency

March 3, 2022

Bayesian Spillover Graphs for Dynamic Networks
Grace Deng, David S. Matteson
Bayesian Network Dynamic Network Probabilistic Time Series Forecasting Spillover Effect Probabilistic Dependency

February 25, 2022

Towards neoRL networks; the emergence of purposive graphs
Per R. Leikanger
Path Breaking Emergence Cognitive Map Probabilistic Dependency Behavior Graph Human Desire

February 24, 2022

Loss as the Inconsistency of a Probabilistic Dependency Graph: Choose Your Model, Not Your Loss Function
Oliver E Richardson
Full Model Loss Function Variational Inference Probabilistic Model Interest Loss Hard to Easy Inconsistency Probabilistic Dependency Probability Divergence

February 21, 2022

Probabilities of the third type: Statistical Relational Learning and Reasoning with Relative Frequencies
Felix Weitkämper
Complex Reasoning Bayesian Network Relative Probability Probabilistic Logic Probabilistic Dependency Statistical Relational

November 16, 2021

Solving Probability and Statistics Problems by Program Synthesis
Leonard Tang, Elizabeth Ke, Nikhil Singh, Nakul Verma, Iddo Drori
Program Synthesis Statistical Query Probability Pr(E1 Probabilistic Program Probabilistic Dependency Level Probability

Probabilistic Dependency

Papers

PieClam: A Universal Graph Autoencoder Based on Overlapping Inclusive and Exclusive Communities

Sequential Conditional Transport on Probabilistic Graphs for Interpretable Counterfactual Fairness

Bayesian Mesh Optimization for Graph Neural Networks to Enhance Engineering Performance Prediction

Circular Belief Propagation for Approximate Probabilistic Inference

Probabilistic Transformer: A Probabilistic Dependency Model for Contextual Word Representation

Inference for Probabilistic Dependency Graphs

Semantic Information Extraction for Text Data with Probability Graph

Uncertainty Quantification of Spatiotemporal Travel Demand with Probabilistic Graph Neural Networks

Hierarchical Inference of the Lensing Convergence from Photometric Catalogs with Bayesian Graph Neural Networks

Inference and dynamic decision-making for deteriorating systems with probabilistic dependencies through Bayesian networks and deep reinforcement learning

Causal Effect Identification in Uncertain Causal Networks

Bayesian Spillover Graphs for Dynamic Networks

Towards neoRL networks; the emergence of purposive graphs

Loss as the Inconsistency of a Probabilistic Dependency Graph: Choose Your Model, Not Your Loss Function

Probabilities of the third type: Statistical Relational Learning and Reasoning with Relative Frequencies

Solving Probability and Statistics Problems by Program Synthesis