International Conference on Machine Learning and Pattern Recognition MLPR on December 04-06, 2026 in Kyoto, Japan

Topics (Topics of interest for submission include, but are not limited to:)

Track 1: Foundations of Machine Learning

- Statistical learning theory and generalization bounds

- Optimization methods for deep learning (adaptive optimizers, loss landscapes)

- Dimensionality reduction and manifold learning

- Graphical models, causal inference, and probabilistic reasoning

- Active learning and query strategies

- Transfer, multi-task, and meta-learning

- Learning from noisy, limited, or imbalanced data

- Reinforcement learning theory and bandit algorithms

Track 2: Deep Learning & Generative Models

- Generative AI (diffusion models, VAEs, GANs, flow-based models)

- Large language models and vision-language models

- Transformer architectures and attention variants

- Self-supervised and foundation model pre-training

- Model compression (pruning, quantization, knowledge distillation)

- Neural architecture search and automated deep learning

- Graph neural networks and geometric deep learning

- Representation learning for video, 3D, and multimodal data

Track 3: Pattern Recognition & Computer Vision

- Feature extraction, selection, and descriptor learning

- Object detection, segmentation, and tracking

- Face, gesture, and action recognition

- Medical image analysis and computational pathology

- Remote sensing image analysis and Earth observation

- Document analysis and handwriting recognition

- Biometric recognition (fingerprint, iris, voice)

- 3D shape analysis and point cloud processing

Track 4: Responsible & Trustworthy AI

- Fairness, accountability, and transparency in ML models

- Explainable AI (XAI) and interpretability methods

- Robustness against adversarial attacks and out-of-distribution inputs

- Privacy-preserving ML (federated learning, differential privacy)

- AI safety, value alignment, and ethical frameworks

- Uncertainty quantification and reliable predictions

- Bias detection and mitigation in datasets and algorithms

- Regulatory compliance and auditable AI systems

Track 5: Applications of ML & Pattern Recognition

- ML for healthcare (diagnosis, drug discovery, genomics)

- Intelligent transportation and autonomous driving

- Natural language processing and speech recognition

- Recommender systems and personalization

- Time-series forecasting (finance, energy, IoT)

- Robotics and embodied AI

- Smart manufacturing and predictive maintenance

- Agriculture, environmental monitoring, and climate science

Track 6: Reinforcement Learning & Decision Intelligence

- Deep reinforcement learning algorithms (DQN, PPO, SAC, TD3)

- Multi-agent reinforcement learning and game-theoretic reasoning

- Inverse reinforcement learning and imitation learning

- Hierarchical reinforcement learning and option frameworks

- Offline reinforcement learning and batch RL

- Reinforcement learning from human feedback (RLHF)

- Sequential decision making under uncertainty (POMDPs, bandits)

- Applications of RL in robotics, autonomous driving, recommendation systems, and game AI

More Details please click: https://www.mlpr.org/cfp.html

Name: International Association of Computer Science and Information Technology
Website: http://www.iacsit.org/