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
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.
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 ctmlconf@163.com
More details about submission, please visit at https://www.ctml.org/submission.html
CONFERENCE SCHEDULE:
November 27, 2026
10:30-17:00 Onsite Sign-in
November 28, 2026
09:00-17:00 Registration
09:00-09:10 Opening Ceremony
09:10-09:55 Keynote 1
09:55-10:30 Group Photo & Coffee Break
10:30-11:15 Keynote 2
11:15-12:00 Keynote 3
12:00-13:30 Conference Lunch
13:30-15:30 Parallel Sessions
15:30-15:45 Coffee Break
15:45-18:00 Parallel Sessions
18:30-21:00 Conference Dinner
November 29, 2026
All day Parallel Sessions
CONTACT
Mary Zhan (Conference Secretary)
E-mail: ctmlconf@163.com
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
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.
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 ctmlconf@163.com
More details about submission, please visit at https://www.ctml.org/submission.html
CONFERENCE SCHEDULE:
November 27, 2026
10:30-17:00 Onsite Sign-in
November 28, 2026
09:00-17:00 Registration
09:00-09:10 Opening Ceremony
09:10-09:55 Keynote 1
09:55-10:30 Group Photo & Coffee Break
10:30-11:15 Keynote 2
11:15-12:00 Keynote 3
12:00-13:30 Conference Lunch
13:30-15:30 Parallel Sessions
15:30-15:45 Coffee Break
15:45-18:00 Parallel Sessions
18:30-21:00 Conference Dinner
November 29, 2026
All day Parallel Sessions
CONTACT
Mary Zhan (Conference Secretary)
E-mail: ctmlconf@163.com
