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

Website https://www.ctml.org/ | Edit Freely

Category Computational Theory and Machine Learning

Deadline: October 20, 2026 | Date: November 27, 2026-November 29, 2026

Venue/Country: Rio de Janeiro, Brazil

Updated: 2026-03-18 16:47:16 (GMT+9)

Call For Papers - CFP

Acronym: CTML 2026

Place: Rio de Janeiro, Brazil

Date: November 27-29, 2026

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

Organizer: Universidade Federal Fluminen

The International Conference on Computational Theory and Machine Learning (CTML 2026) will be held in Rio de Janeiro, Brazil, during November 27-29, 2026.

This conference focuses on computational theory, machine learning foundations, algorithm design, and related interdisciplinary research. It provides a high-level academic platform for global researchers, scholars, and practitioners to share innovative findings, exchange ideas, and promote academic cooperation. CTML 2026 welcomes high-quality papers and presentations, aiming to advance theoretical progress and practical applications in computational theory and machine learning.

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 ctmlconf163.com

More details about submission, please visit at https://www.ctml.org/submission.html

CONTACT

Mary Zhan (Conference Secretary)

E-mail: ctmlconf163.com