5th International Conference on Machine Learning, NLP and Data Mining (MLDA 2026)
July 29 ~ 30, 2026, Virtual Conference Scope & Topics 5th International Conference on Machine Learning, NLP and Data Mining (MLDA 2026) provides a premier international forum for researchers, practitioners and industry experts to share their latest findings, methodologies and innovations in Machine Learning, Natural Language Processing and Data Mining. As these fields continue to evolve rapidly and shape the future of intelligent systems, MLDA 2026 aims to bring together diverse perspectives that advance both theoretical foundations and real world applications.
The conference welcomes high quality submissions that present original research results, innovative projects, comprehensive surveys and industrial case studies. Contributions may address significant advances in learning algorithms, language technologies, data centric methods, scalable systems and interdisciplinary applications. While the conference highlights key areas within ML, NLP and Data Mining, it remains open to emerging topics and novel ideas that push the boundaries of intelligent data driven research.
MLDA 2026 encourages participation from academia, industry and government, fostering collaboration and knowledge exchange across disciplines. By providing a platform for discussing breakthroughs, challenges and future directions, the conference aims to support the continued growth and impact of machine learning, natural language computing and data mining in science, technology and society. Topics of interest include, but are not limited to, the following - Machine learning theory, optimization and probabilistic modeling
- Bayesian learning, causal inference and causal representation learning
- Semi supervised, weakly supervised and self supervised learning
- Continual, lifelong, transfer, meta learning and few shot learning
- NeuroAI and neuroscience inspired machine learning
- Deep neural architectures and efficient deep learning (pruning, quantization, distillation)
- Multimodal representation learning across text, vision, audio and sensor data
- Graph neural networks, graph transformers and temporal graph learning
- Large scale, distributed and parallel training of ML models
- Pretraining, fine tuning and adaptation of large language models (LLMs)
- Retrieval augmented generation (RAG), memory augmented models and long context modeling
- LLM agents, tool using models, multi step reasoning and autonomous language agents
- Hallucination mitigation, LLM evaluation, safety, alignment and responsible LLMs
- Information extraction, information retrieval and retrieval enhanced NLP
- Text mining, text classification, topic modeling and semantic parsing
- Argumentation mining, opinion mining and sentiment analysis
- Corpus linguistics, multilingual NLP and cross lingual modeling
- Dialogue systems, conversational agents, question answering andchatbots
- Speech recognition, speech synthesis and affective speech technologies
- Data mining foundations, pattern mining, sequence mining and knowledge discovery
- Data centric AI, dataset auditing, bias detection and data governance
- Synthetic data generation, fidelity evaluation and data augmentation pipelines
- Big data analytics, scalable data processing and distributed data systems
- Knowledge graphs, semantic reasoning and linked data technologies
- Predictive learning, forecasting and advanced time series machine learning
- Time series modeling for irregular, multivariate and high frequency data
- Recommendation systems, personalization and user modeling
- Machine learning for business intelligence, decision support and knowledge intensive systems
- Image and video understanding, scene analysis and object recognition
- Vision language models, multimodal fusion and embodied perception
- Deep reinforcement learning, multi agent RL, offline RL and imitation learning
- RL for NLP, dialogue systems, planning and sequential decision making
- Explainable AI, interpretability, fairness, accountability and transparency
- Robustness, adversarial machine learning and secure model training
- Privacy preserving ML, federated learning and differential privacy
- ML pipelines, automation, deployment, monitoring and optimization
- MLOps, scalable ML systems, distributed computing and infrastructure for LLMs
- Model integrity, watermarking, secure inference and model protection
- ML and NLP for IoT, edge computing, TinyML and ubiquitous intelligence
- Semantic web technologies, ontologies, conceptual modeling and knowledge integration
- Code generation, program synthesis, automated debugging and software engineering ML
- ML for healthcare, bioinformatics, computational biology and medical applications
- ML for finance, economics, business analytics and risk modeling
- ML for education, learning analytics and personalized learning systems
- ML for environmental modeling, sustainability and climate related applications
- ML for engineering, scientific discovery and simulation driven research
- Social network analysis, web mining and web intelligence
- Human AI collaboration, human in the loop learning and interactive ML
- Affective computing, emotion aware systems and user centric AI design
Paper Submission Authors are invited to submit papers through the conference Submission System by July 04, 2026. Submissions must be original and should not have been published previously or be under consideration for publication while being evaluated for this conference. The proceedings of the conference will be published by The proceedings of the conference will be published by International Journal on Cybernetics & Informatics (IJCI)(Confirmed). Selected papers from MLDA 2026, after further revisions, will be published in the special issue of the following journals. Important Dates | Submission Deadline | : | July 04, 2026 | | Authors Notification | : | July 15, 2026 | | Final Manuscript Due | : | July 18, 2026 |
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