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

Acronym: CTML 2026

Place: Rio de Janeiro, Brazil

Date: November 27-29, 2026

Website: https://www.ctml.org/

Organizer: India International Congress on Computational Intelligence(IICCI)

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 Session