2026 IEEE 9th International Conference on Machine Learning and Natural Language Processing (MLNLP 2026)

Call for Papers Topics:
Track I: Foundations of Machine Learning:
Supervised, unsupervised, and semi-supervised learning
Deep Learning: neural network architectures, attention mechanisms, graph neural networks
Learning Paradigms: reinforcement learning, transfer learning, multi-task learning
Trustworthy AI: explainable AI, robustness, fairness, privacy preservation
Optimization Algorithms: large-scale optimization, evolutionary computation
Edge & Distributed ML: federated learning, on-device learning

Track II: Natural Language Processing (NLP):
Large Language Models: pretraining, fine-tuning, prompt engineering, alignment
Linguistic Analysis: syntactic, semantic, and pragmatic analysis
Core NLP Tasks: named entity recognition, sentiment analysis, text summarization
Dialogue & Interactive Systems: chatbots, multi-turn dialogue management, human-computer interaction
Machine Translation: neural MT, multilingual processing
Information Retrieval: QA systems, document ranking, knowledge graph construction

Track III: Cybersecurity & Privacy Protection (Emerging Trends):
Multimodal Learning: fusion of vision, audio, and text
Cognitive Computing: human-cognition-inspired language understanding
AI for Science: ML/NLP applications in healthcare, finance, and social sciences
Efficiency & Sustainability: green AI, model compression, efficient inference
Creative AI: controllable text generation, AI-assisted content creation
Ethics & Society: AI-generated content detection (e.g., deepfake text), bias mitigation