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

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

June 20 ~ 21, 2026, Sydney, Australia

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.

 

·         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 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

·         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

·         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 Inference and Causal Discovery

·         Causal Representation Learning

·         Counterfactual Reasoning

·         Explainable and Interpretable Machine Learning

·         Deep Learning for Time Series Forecasting

·         Streaming Data and Online Prediction

·         Event Based and Temporal Modeling

·         Sequential and Structured Data Analysis

·         Neural Differential Equations

·         ML for Physics, Chemistry, Biology and Engineering

·         ML for Scientific Discovery, Simulation and Surrogate Modeling

·         Physics Informed Machine Learning

·         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)

·         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 and TinyML

·         Robot Learning and Policy Optimization

·         Embodied Agents and Perception Action Loops

·         Sim to Real Transfer

·         Learning for Autonomous Systems

·         Code Generation and Repair

·         Program Synthesis and Verification

·         ML Assisted Software Development

·         Multimodal Code Understanding

·         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 Optimization Layers

·         Implicit Neural Representations and Equilibrium Models

·         Differentiable Physics and Simulation

·         End to End Differentiable Pipelines

·         Autonomous ML Agents and Tool Using Systems

·         Multi Agent Learning, Cooperation and Negotiation

·         Planning + Reasoning + Acting Loops

·         Agentic Evaluation and Safety Frameworks

·         Quantum Inspired ML Algorithms

·         Hybrid Quantum Classical Models

·         Quantum Optimization and Simulation

·         Protein and Molecule Design with ML

·         DNA/RNA Sequence Modeling

·         ML for Gene Editing and Synthetic Biology

·         Biological Foundation Models

·         Market Simulation and Prediction

·         Mechanism Design and Auctions

·         Game Theoretic Machine Learning

·         ML for Economic Forecasting

·         ML for Cloud and Distributed Systems

·         ML for Networking, Routing and Traffic Optimization

·         ML for Resource Allocation and Scheduling

·         Satellite Imagery and Remote Sensing ML

·         Geospatial Forecasting and Mapping

·         Climate Modeling and Environmental ML

·         Pattern Mining and Anomaly Detection

·         Predictive Modeling and Forecasting

·         Large Scale Data Mining and Big Data Analytics

·         Knowledge Discovery in Databases (KDD)

·         Healthcare, Bioinformatics and Drug Discovery

·         Finance, Economics and Risk Modeling

·         Cybersecurity and Threat Detection

·         Social Media, Behavior Modeling and Misinformation

·         Education, Personalization and Learning Analytics

·         Industrial Systems, IoT and Smart Manufacturing

·         ML Evaluation Metrics and Benchmark Design

·         Reproducibility, Transparency and Open Science

·         Dataset Governance, Quality and Bias Detection

·         Model Auditing and Performance Diagnostics

·         Fairness, Bias and Ethical AI

·         AI Governance, Regulation and Policy Frameworks

·         Societal Impact and Responsible Deployment

·         Human Centered and Human AI Collaborative Systems

 

Authors are invited to submit papers through the conference Submission System by May 02, 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 MLT 2026, after further revisions, will be published in the special issues of the following journals.

 

·         Machine Learning and Applications: An International Journal (MLAIJ)

·         International Journal of Artificial Intelligence & Applications (IJAIA)

·         Submission Deadline: May 02, 2026

·         Authors Notification: May 23, 2026

·         Registration & Camera-Ready Paper Due: May 30, 2026

 

 

Here's where you can reach us: mlt@sai2026.org (or) mltconfere@yahoo.com

 

For more details, please visit: https://sai2026.org/mlt/index

 

Paper Submission Link: https://icaita2026.org/submission/index.php