7th International Conference on Machine Learning & Trends (MLT 2026)

Memo

7th International Conference on Machine Learning & Trends (MLT 2026) June 20 ~ 21, 2026, Sydney, Australia https://sai2026.org/mlt/index Scope 7th International Conference on Machine Learning & Trends (MLT 2026) serves as a premier global forum for presenting and exchanging the latest advancements in Machine Learning theory, methodologies, and real world applications. As machine learning continues to shape the future of intelligent systems, scientific discovery, and industry innovation, MLT 2026 aims to bring together leading researchers, practitioners, and industry experts to explore emerging trends and transformative breakthroughs in the field. The conference provides a dynamic platform for fostering collaboration between academia and industry, encouraging the cross pollination of ideas that drive the next generation of machine learning technologies. Participants will have the opportunity to engage with cutting edge research, discuss open challenges, and identify new directions that will influence the evolution of ML in the years ahead. Authors are invited to contribute high quality submissions that showcase original research results, innovative projects, comprehensive surveys, and industrial case studies demonstrating significant progress in machine learning and its rapidly expanding ecosystem. Contributions may address, but are not limited to, the broad range of topics outlined below.   Topics of interest Machine Learning Foundations Supervised, Unsupervised and Semi Supervised Learning Reinforcement Learning and Sequential Decision Making Probabilistic Modeling and Bayesian Machine Learning Optimization Methods for Machine Learning Learning Theory, Generalization and Sample Efficiency Representation Learning and Feature Learning Deep Learning and Neural Architectures Deep Neural Networks and Training Dynamics Transformers and Attention Based Models Graph Neural Networks (GNNs) and Graph Transformers Self Supervised and Contrastive Learning Neural Architecture Search (NAS) Foundation Models and Large Scale Pretraining Generative Models and Synthetic Data Diffusion Models and Score Based Generative Models Generative Adversarial Networks (GANs) Synthetic Data Generation and Data Centric AI Generative Modeling for Images, Text, Audio, Video and Multimodal Data   Advanced Learning Paradigms Meta Learning and Few Shot Learning Continual, Lifelong and Online Learning Multi Task and Transfer Learning Active Learning and Curriculum Learning Federated, Distributed and Collaborative Learning Causal and Explainable Machine Learning Causal Inference and Causal Discovery Causal Representation Learning Counterfactual Reasoning Explainable and Interpretable Machine Learning Time Series, Forecasting and Sequential Modeling Deep Learning for Time Series Forecasting Streaming Data and Online Prediction Event Based and Temporal Modeling Sequential and Structured Data Analysis Scientific Machine Learning (SciML)   Neural Differential Equations ML for Physics, Chemistry, Biology and Engineering ML for Scientific Discovery, Simulation and Surrogate Modeling Physics Informed Machine Learning ML Security, Safety and Robustness Adversarial Attacks and Defenses Model Extraction, Poisoning and Evasion Attacks Secure and Trustworthy ML Pipelines Safety, Reliability and Risk Aware ML ML for Safety Critical Systems (healthcare, aviation, autonomous driving) Scalable, Efficient and Systems Level ML Efficient Training: Compression, Pruning, Quantization Large Scale ML Systems and Distributed Training Hardware Aware ML (GPUs, TPUs, Edge Devices) Energy Efficient and Sustainable ML Real Time ML, Edge ML andTinyML Robotics, Embodied AI and Control   Robot Learning and Policy Optimization Embodied Agents and Perception Action Loops Sim to Real Transfer Learning for Autonomous Systems ML for Code, Software Engineering and Program Synthesis Code Generation and Repair Program Synthesis and Verification ML Assisted Software Development Multimodal Code Understanding Multimodal Learning, Vision and Perception Computer Vision and Visual Recognition Vision Language Models and Multimodal Fusion 3D Vision, Scene Understanding and Embodied Perception Audio, Speech and Sensor Based Learning Differentiable Programming and Implicit Models Differentiable Optimization Layers Implicit Neural Representations and Equilibrium Models Differentiable Physics and Simulation End to End Differentiable Pipelines   Agentic AI and Autonomous ML Systems Autonomous ML Agents and Tool Using Systems Multi Agent Learning, Cooperation and Negotiation Planning + Reasoning + Acting Loops Agentic Evaluation and Safety Frameworks Quantum Machine Learning Quantum Inspired ML Algorithms Hybrid Quantum Classical Models Quantum Optimization and Simulation ML for Biology, Medicine and Synthetic Bio Design Protein and Molecule Design with ML DNA/RNA Sequence Modeling ML for Gene Editing and Synthetic Biology Biological Foundation Models ML for Economics, Markets and Mechanism Design Market Simulation and Prediction Mechanism Design and Auctions Game Theoretic Machine Learning ML for Economic Forecasting   ML for Infrastructure, Networking and Systems Optimization ML for Cloud and Distributed Systems ML for Networking, Routing and Traffic Optimization ML for Resource Allocation and Scheduling Geospatial, Earth Observation and Climate ML Satellite Imagery and Remote Sensing ML Geospatial Forecasting and Mapping Climate Modeling and Environmental ML Data Mining, Knowledge Discovery and Predictive Analytics Pattern Mining and Anomaly Detection Predictive Modeling and Forecasting Large Scale Data Mining and Big Data Analytics Knowledge Discovery in Databases (KDD) Applied Machine Learning Across Domains Healthcare, Bioinformatics and Drug Discovery Finance, Economics and Risk Modeling Cybersecurity and Threat Detection Social Media, BehaviorModeling and Misinformation Education, Personalization and Learning Analytics Industrial Systems, IoT and Smart Manufacturing   Evaluation, Benchmarking and Reproducibility ML Evaluation Metrics and Benchmark Design Reproducibility, Transparency and Open Science Dataset Governance, Quality and Bias Detection Model Auditing and Performance Diagnostics AI Governance, Ethics and Societal Impact Fairness, Bias and Ethical AI AI Governance, Regulation and Policy Frameworks Societal Impact and Responsible Deployment Human Centered and Human AI Collaborative Systems Paper Submission Authors are invited to submit papers through the conference Submission System by June 06, 2026 (Final Call). 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 MLT 2026, after further revisions, will be published in the special issue of the following journals. Machine Learning and Applications: An International Journal (MLAIJ) International Journal of Artificial Intelligence & Applications (IJAIA) International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI) International Journal on Soft Computing (IJSC) International Journal of Fuzzy Logic Systems (IJFLS) Advances in Vision Computing: An International Journal (AVC) The International Journal of Ambient Systems and Applications (IJASA) Important Dates (2nd batch : submissions after May 11th) Submission Deadline : June 06, 2026 (Final Call) Authors Notification : June 15, 2026 Registration & Camera-Ready Paper Due : June 18, 2026 Contact Us Here’s where you can reach us : This email address is being protected from spambots. You need JavaScript enabled to view it. (or) This email address is being protected from spambots. You need JavaScript enabled to view it.