Cover Image

Auto-ML Graph Neural Network Hypermodels for Outcome Prediction in Event-Sequence Data

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Wang, Fang, Kosca, Lance, Kosca, Adrienne, Gacesa, Marko, Damiani, Ernesto
Format:	Preprint
Published:	2025
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Leveraging Duration Pseudo-Embeddings in Multilevel LSTM and GCN Hypermodels for Outcome-Oriented PPM
by: Wang, Fang, et al.
Published: (2025)

HGCN(O): A Self-Tuning GCN HyperModel Toolkit for Outcome Prediction in Event-Sequence Data
by: Wang, Fang, et al.
Published: (2025)

Time-Aware and Transition-Semantic Graph Neural Networks for Interpretable Predictive Business Process Monitoring
by: Wang, Fang, et al.
Published: (2025)

Physicochemical properties of lunar regolith simulant for in situ oxygen production
by: De Guzman, Alyssa Ang, et al.
Published: (2026)

Semi-strong Efficient Market of Bitcoin and Twitter: an Analysis of Semantic Vector Spaces of Extracted Keywords and Light Gradient Boosting Machine Models
by: Wang, Fang, et al.
Published: (2024)

Comprehensive Attribute Encoding and Dynamic LSTM HyperModels for Outcome Oriented Predictive Business Process Monitoring
by: Wang, Fang, et al.
Published: (2025)

A Visualized Malware Detection Framework with CNN and Conditional GAN
by: Wang, Fang, et al.
Published: (2024)

An AutoML-based approach for Network Intrusion Detection
by: Gyimah, Nana Kankam, et al.
Published: (2024)

Grad-Instructor: Universal Backpropagation with Explainable Evaluation Neural Networks for Meta-learning and AutoML
by: Ino, Ryohei
Published: (2024)

LightAutoDS-Tab: Multi-AutoML Agentic System for Tabular Data
by: Lapin, Aleksey, et al.
Published: (2025)

Robustness of AutoML on Dirty Categorical Data
by: Bueno, Marcos L. P., et al.
Published: (2026)

Graph Neural Networks for Carbon Dioxide Adsorption Prediction in Aluminium-Exchanged Zeolites
by: Petković, Marko, et al.
Published: (2024)

AutoGluon-Multimodal (AutoMM): Supercharging Multimodal AutoML with Foundation Models
by: Tang, Zhiqiang, et al.
Published: (2024)

Predicting Mortgage Default with Machine Learning: AutoML, Class Imbalance, and Leakage Control
by: Hu, Xianghong, et al.
Published: (2026)

LEMUR Neural Network Dataset: Towards Seamless AutoML
by: Goodarzi, Arash Torabi, et al.
Published: (2025)

EDCA - An Evolutionary Data-Centric AutoML Framework for Efficient Pipelines
by: Simões, Joana, et al.
Published: (2025)

Bag of Tricks for Multimodal AutoML with Image, Text, and Tabular Data
by: Tang, Zhiqiang, et al.
Published: (2024)

Auto-ADMET: An Effective and Interpretable AutoML Method for Chemical ADMET Property Prediction
by: de Sá, Alex G. C., et al.
Published: (2025)

TNPAR: Topological Neural Poisson Auto-Regressive Model for Learning Granger Causal Structure from Event Sequences
by: Liu, Yuequn, et al.
Published: (2023)

Heterogeneous Graph Sequence Neural Networks for Dynamic Traffic Assignment
by: Liu, Tong, et al.
Published: (2024)

AutoLL: Automatic Linear Layout of Graphs based on Deep Neural Network
by: Watanabe, Chihiro, et al.
Published: (2021)

AutoSGNN: Automatic Propagation Mechanism Discovery for Spectral Graph Neural Networks
by: Mo, Shibing, et al.
Published: (2024)

Assessing the Use of AutoML for Data-Driven Software Engineering
by: Calefato, Fabio, et al.
Published: (2023)

Optimal Pricing for Data-Augmented AutoML Marketplaces
by: Han, Minbiao, et al.
Published: (2023)

Data Pipeline Training: Integrating AutoML to Optimize the Data Flow of Machine Learning Models
by: Wu, Jiang, et al.
Published: (2024)

Explaining AutoClustering: Uncovering Meta-Feature Contribution in AutoML for Clustering
by: da Silva, Matheus Camilo, et al.
Published: (2026)

Confidence Interval Estimation of Predictive Performance in the Context of AutoML
by: Paraschakis, Konstantinos, et al.
Published: (2024)

Integration of Graph Neural Network and Neural-ODEs for Tumor Dynamic Prediction
by: Bazgir, Omid, et al.
Published: (2023)

Categorical Distributions are Effective Neural Network Outputs for Event Prediction
by: Doran, Kevin, et al.
Published: (2025)

Similarity-Navigated Conformal Prediction for Graph Neural Networks
by: Song, Jianqing, et al.
Published: (2024)

Pretext Training Algorithms for Event Sequence Data
by: Wang, Yimu, et al.
Published: (2024)

Self-explainable Graph Neural Network for Alzheimer's Disease And Related Dementias Risk Prediction
by: Hu, Xinyue, et al.
Published: (2023)

Symmetry-Informed Graph Neural Networks for Carbon Dioxide Isotherm and Adsorption Prediction in Aluminum-Substituted Zeolites
by: Petković, Marko, et al.
Published: (2025)

Problem-oriented AutoML in Clustering
by: da Silva, Matheus Camilo, et al.
Published: (2024)

The Potential of AutoML for Recommender Systems
by: Vente, Tobias, et al.
Published: (2024)

Graph Sparsification for Enhanced Conformal Prediction in Graph Neural Networks
by: He, Yuntian, et al.
Published: (2024)

Norm Augmented Graph AutoEncoders for Link Prediction
by: Liu, Yunhui, et al.
Published: (2025)

RF-Informed Graph Neural Networks for Accurate and Data-Efficient Circuit Performance Prediction
by: Asadi, Anahita, et al.
Published: (2025)

Inductive Spatial Temporal Prediction Under Data Drift with Informative Graph Neural Network
by: Zheng, Jialun, et al.
Published: (2024)

Mitigating Degree Bias in Signed Graph Neural Networks
by: He, Fang, et al.
Published: (2024)