2026 International Conference on Computational Theory and Machine Learning (CTML 2026)

PUBLICATION Submissions will be reviewed by the conference technical committees, and accepted papers will be published in Conference Proceedings and submitted to EI Compendex, Scopus, etc. for indexing. CALL FOR PAPERS Authors are invited to submit full papers describing original research work in areas including, but not limited to: TRACK 1: Foundations of Computational Learning Statistical Learning Theory and Generalization Computational Complexity of Learning Online Learning and Regret Analysis Learning Dynamics and Convergence Scaling Laws and Emergent Behavior in Large Models PAC-Bayes Theory and Algorithmic Stability TRACK 2: Deep Learning Theory and Neural Architectures Expressivity and Capacity of Neural Networks Theoretical Analysis of Transformers and Foundation Models Neural Network Optimization Landscapes Overparameterization and Double Descent Implicit Regularization and Bias of Gradient Methods Mechanistic Interpretability and Model Internals Physics-Informed Neural Networks and Neural Operators TRACK 3: Optimization and Algorithms for Machine Learning Convex and Non-Convex Optimization Evolutionary Algorithms and Metaheuristics Combinatorial Optimization in Learning Surrogate-Assisted and Expensive Optimization Optimization for Resource-Constrained Settings Federated Learning and Distributed Optimization Multi-Objective Optimization in ML Systems TRACK 4: Graph Theory, Combinatorics, and Learning on Structures Graph Neural Networks Theory Spectral Graph Theory and Applications Algorithmic Graph Theory and Network Analysis Combinatorial Optimization with Learning Random Graphs and Probabilistic Methods Geometric Deep Learning Learning on Manifolds and Non-Euclidean Data TRACK 5: Trustworthy and Explainable AI Causal Inference and Discovery Explainability and Interpretability Robustness, Uncertainty, and Calibration Privacy and Fairness in Machine Learning Adversarial Machine Learning Distribution Shift and Domain Generalization Safety and Alignment of AI Systems TRACK 6: AI for Scientific Discovery and Emerging Frontiers AI for Scientific Discovery Quantum Machine Learning Symbolic Regression and Scientific Law Discovery AI-Accelerated Scientific Computing Multi-Modal Learning and Fusion Computational Biology and AI for Healthcare Climate Modeling and Environmental AI TRACK 7: Efficient and Scalable Machine Learning Systems Model Compression and Knowledge Distillation Quantization and Pruning Neural Architecture Search Edge AI and TinyML Green AI and Energy-Efficient Learning Large-Scale Training Systems ML Compilers and Hardware-Software Co-Design For details about topics, please visit at https://www.ctml.org/cfp.html SUBMISSION 1. Full Paper (Publication and Presentation) 2. Abstract (Presentation Only) For full paper(.pdf), please upload to https://www.zmeeting.org/submission/ctml2026 For abstract, please send it to [email protected] More details about submission, please visit at https://www.ctml.org/submission.html CONTACT Mary Zhan (Conference Secretary) E-mail: [email protected]