Name: 8th International Conference on Machine Learning & Applications (CMLA 2026)
Start: 2026-07-16T10:00:00+01:00
End: 2026-07-17T18:00:00+01:00
Location: London

8th International Conference on Machine Learning & Applications (CMLA 2026)

July 16 ~ 17, 2026, London, United Kingdom

https://cmla2026.org/

Scope

The 8th International Conference on Machine Learning & Applications (CMLA 2026) provides a premier global forum for researchers, practitioners, and industry experts to share the latest advances in machine learning theory, methodologies, and real world applications. As machine learning continues to transform science, engineering, business, and society, CMLA 2026 aims to bring together a diverse community to exchange ideas, present innovative research, and explore emerging challenges and opportunities in the field.

The conference welcomes high quality contributions that demonstrate significant progress in foundational machine learning, cutting edge algorithms, large scale systems, and domain specific applications. Authors are invited to submit original research articles, case studies, survey papers, and industrial experiences that highlight meaningful advances in machine learning and its rapidly expanding ecosystem.

CMLA 2026 encourages submissions across a broad range of topics, including but not limited to the areas listed below. By fostering collaboration between academia, industry, and research institutions, the conference seeks to accelerate innovation, deepen scientific understanding, and support the development of next generation machine learning technologies.

Topics of interest

Foundations of Machine Learning

Statistical Learning Theory and Generalization
Optimization for ML (Convex, Non Convex, Large Scale)
Probabilistic Modeling, Bayesian Learning and Graphical Models
Causal Inference, Causal ML and Counterfactual Reasoning
Online Learning, Meta Learning and Continual Learning
Multi Task Learning, Transfer Learning and Domain Adaptation
Theory of Deep Learning and Emergent Behaviors

Deep Learning and Representation Learning

Neural Network Architectures and Training Techniques
Self Supervised Learning and Contrastive Learning
Generative Models (GANs, Diffusion Models, VAEs)
Diffusion Models for Images, Text, Time Series, Molecules and Graphs
Foundation Models, LLMs, Vision Language Models and Multimodal Models
Efficient Deep Learning (Pruning, Quantization, Distillation)
Representation Learning for Structured, Temporal and Graph Data

Reinforcement Learning, Decision Making and Embodied AI

Deep Reinforcement Learning and Policy Optimization
Multi Agent RL, Game Theory and Coordination
Offline RL, Safe RL and Risk Sensitive RL
World Models, Embodied AI and Interactive Learning
RL for Robotics, Control Systems and Real World Deployment
Hierarchical RL and Skill Discovery
Planning Augmented Models and Decision Transformers

Natural Language Processing, Speech and Multimodal AI

Large Language Models and Instruction Tuned Models
Retrieval Augmented Generation (RAG) and Knowledge Grounded Models
Long Context Models, Memory Augmented Models and Tool Using LLMs
Text Generation, Summarization and Dialogue Systems
Speech Recognition, Speech Synthesis and Audio Language Models
Vision Language Models, Video Language Models and Multimodal Fusion
NLP for Low Resource Languages and Cross Lingual Learning

Computer Vision, Perception and Graphics

Image Classification, Detection and Segmentation
3D Vision, Scene Understanding and SLAM
Vision Transformers and Diffusion Based Vision Models
Video Understanding, Action Recognition and Motion Prediction
Generative Vision Models, Neural Rendering and 3D Generation
Embodied Perception and Interactive Vision
Vision Language Action Models for Robotics

Data Mining, Knowledge Discovery and Graph Learning

Graph Neural Networks (GNNs) and Graph Representation Learning
Knowledge Graphs, Reasoning and Neuro Symbolic AI
Large Scale Data Mining and Pattern Discovery
Time Series Forecasting, Anomaly Detection and Predictive Modeling
Simulation Based ML and Synthetic Data Generation
ML for Structured, Relational and Heterogeneous Data

Trustworthy, Explainable and Responsible AI

Explainable AI (XAI) and Mechanistic Interpretability
Fairness, Accountability, Transparency and Ethics in ML
Robust ML, Adversarial Attacks and Defenses
Jailbreak Resistant LLMs and Safety Evaluation
Privacy Preserving ML (Differential Privacy, Federated Learning, Secure ML)
Safety Critical ML and Reliability
AI Governance, Risk Assessment and Policy Aligned ML

ML Systems, Hardware Acceleration and Efficient Computing

Distributed and Parallel ML Systems
Training and Inference Optimization for Foundation Models
ML Compilers, Optimization and Deployment Frameworks
Edge ML, TinyML and On Device Learning
Edge Native Foundation Models and Distributed Inference
Neuromorphic Computing and Brain Inspired ML
Energy Efficient ML, Green AI and Carbon Aware ML Pipelines

Applied Machine Learning and Domain Specific ML

Healthcare and Life Sciences
Medical Imaging, Diagnostics and Clinical Decision Support
Computational Biology, Genomics and Drug Discovery
Digital Health, Wearables and Personalized Medicine
ML for Neuroscience and Cognitive Modeling
ML for Digital Therapeutics and Clinical Decision Automation

Science and Engineering

ML for Physics, Chemistry, Materials Science and Climate Modeling
Physics Informed ML and Scientific Machine Learning (SciML)
Differentiable Physics, Neural Simulators and ML Accelerated Simulation
ML for Robotics, Autonomous Systems and Control
ML for Smart Cities, IoT and Cyber Physical Systems

Business, Finance and Social Systems

ML for Finance, Risk Modeling and Fraud Detection
Recommender Systems, Personalization and User Modeling
Social Network Analysis and Computational Social Science
ML for Policy Simulation and Societal Impact Modeling

Emerging Trends

Agentic AI, Autonomous AI Systems and Multi Agent LLM Ecosystems
Tool Using AI, Planning Augmented LLMs and Autonomous Agents
Program Synthesis, AI for Code and ML Guided Theorem Proving
Quantum Machine Learning and Quantum Inspired Algorithms
AutoML, Neural Architecture Search (NAS) and Hyperparameter Optimization
ML for Foundation Model Alignment, Safety and Governance
ML for Autonomous Scientific Discovery and Robot Scientists
ML for Synthetic Biology, Bio Inspired Algorithms and Living Systems

Paper Submission

Authors are invited to submit papers through the conference Submission System by May 09, 2026. Submissions must be original and should not have been published previously or be under consideration for publication while being evaluated for this conference. The proceedings of the conference will be published by Computer Science Conference Proceedings (H index 46) in Computer Science & Information Technology (CS & IT) series (Confirmed).

Selected papers from CMLA 2026, after further revisions, will be published in the special issue of the following journals