International Conference on Computational Theory and Machine Learning CTML on November 27-29, 2026 in Rio de Janeiro, Brazil

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

Name: etpub