PAKDD 2026: Pacific-Asia Conference on Knowledge Discovery and Data Mining
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Día de Entrega:
2025-11-15
Fecha de Notificación:
2026-02-08
Fecha de Conferencia:
2026-06-09
Ubicación:
Hong Kong, China
Años:
30
CCF: c CORE: a Vistas: 349315 Seguidores: 312 Asistentes: 78
Solicitud de Artículos
PAKDD 2026 (Pacific-Asia Conference on Knowledge Discovery and Data Mining) is a CCF C / CORE A conference held in Hong Kong, China on 2026-06-09. The paper submission deadline is 2025-11-15. Acceptance notifications are sent on 2026-02-08.
Topics of relevance for the conference include, but are not limited to, the following:
Note: Papers related to the large language models (LLMs) could be submitted to "Special Track on LLMs for Data Science".
Theoretical Foundations
Generative AI, quantum ML, neuro-symbolic methods and reasoning, causal reasoning, non-IID learning, OOD generalization, representation learning, mathematical and statistical foundations, information theoretic approaches, optimization method
Theoretical foundations for fairness, trustworthy AI, safety, model explainability, and XAI
Learning Methods and Algorithms
Clustering, classification, pattern mining and association rules discovery
Supervised learning, semi-supervised learning, few-shot and zero-shot learning, active learning
Reinforcement learning and bandits
Transfer learning, federated learning
Anomaly detection, outlier detection
Learning in recommendation engines
Learning in streams and in time series
Learning on structured data, images, texts and multi-modal data
Online learning, model adaption
Graph mining and Graph NNs
Trustworthy Machine Learning
Fairness
Data Processing for Learning
Dimensionality reduction, feature extraction, subspace construction
Data cleaning and preparation, data integration and summarization
Learning in real-time
Big data technologies
Information retrieval
Data/entity/event/relationship extraction
User interfaces and visual analytics
Security, Privacy, Ethics, Information Integrity and Social Issues
Modeling credibility, trustworthiness, and reliability
Privacy-preserving data mining and privacy models
Model transparency, interpretability, and fairness
Misinformation detection, monitoring, and prevention
Social issues, such as health inequities, social development, and poverty
Interdisciplinary Research on Data Science Applications
Social network/media analysis and dynamics, reputation, influence, trust, opinion mining, sentiment analysis, link prediction, and community detection
Symbiotic human-AI interaction, human-agent collaboration, socially interactive robots, and affective computing
Internet of Things, logistics management, network traffic and log analysis, and supply chain management
Business and financial data, computational advertising, customer relationship management, intrusion and fraud detection, and intelligent assistants
Urban computing, spatial data science and pervasive computing
Medical and public health applications, drug discovery, healthcare management, and epidemic monitoring and prevention
Methods for detecting and combating spamming, trolling, aggression, toxic online behaviors, bullying, hate speech, and low-quality and offensive content
Climate, ecological, and environmental science, and resilience and sustainability
Astronomy and astrophysics, genomics and bioinformatics, high energy physics, robotics, AI-assisted programming, and scientific data