7th International Conference on Machine Learning & Trends (MLT 2026)
Local: Sydney, Australia
Data do evento: 20/06/2026 a 21/06/2026
Prazo de submissão de trabalhos: 06/06/2026
7thInternational Conference on Machine Learning & Trends (MLT 2026)June 20 ~ 21, 2026, Sydney, AustraliaHybrid -- Registered authors can present their work online or face to face.Scope & Topics7thInternational 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 include, but are not limited to, the followingMachine Learning FoundationsSupervised, Unsupervised and Semi Supervised LearningReinforcement Learning and Sequential Decision MakingProbabilistic Modeling and Bayesian Machine LearningOptimization Methods for Machine LearningLearning Theory, Generalization and Sample EfficiencyRepresentation Learning and Feature LearningDeep Learning and Neural ArchitecturesDeep Neural Networks and Training DynamicsTransformers and Attention Based ModelsGraph Neural Networks (GNNs) and Graph TransformersSelf Supervised and Contrastive LearningNeural Architecture Search (NAS)Foundation Models and Large Scale PretrainingGenerative Models and Synthetic DataDiffusion Models and Score Based Generative ModelsGenerative Adversarial Networks (GANs)Synthetic Data Generation and Data Centric AIGenerative Modeling for Images, Text, Audio, Video and Multimodal DataAdvanced Learning ParadigmsMeta Learning and Few Shot LearningContinual, Lifelong and Online LearningMulti Task and Transfer LearningActive Learning and Curriculum LearningFederated, Distributed and Collaborative LearningCausal and Explainable Machine LearningCausal Inference and Causal DiscoveryCausal Representation LearningCounterfactual ReasoningExplainable and Interpretable Machine LearningTime Series, Forecasting and Sequential ModelingDeep Learning for Time Series ForecastingStreaming Data and Online PredictionEvent Based and Temporal ModelingSequential and Structured Data AnalysisScientific Machine Learning (SciML)Neural Differential EquationsML for Physics, Chemistry, Biology and EngineeringML for Scientific Discovery, Simulation and Surrogate ModelingPhysics Informed Machine LearningML Security, Safety and RobustnessAdversarial Attacks and DefensesModel Extraction, Poisoning and Evasion AttacksSecure and Trustworthy ML PipelinesSafety, Reliability and Risk Aware MLML for Safety Critical Systems (healthcare, aviation, autonomous driving)Scalable, Efficient and Systems Level MLEfficient Training: Compression, Pruning, QuantizationLarge Scale ML Systems and Distributed TrainingHardware Aware ML (GPUs, TPUs, Edge Devices)Energy Efficient and Sustainable MLReal Time ML, Edge ML andTinyMLRobotics, Embodied AI and ControlRobot Learning and Policy OptimizationEmbodied Agents and Perception Action LoopsSim to Real TransferLearning for Autonomous SystemsML for Code, Software Engineering and Program SynthesisCode Generation and RepairProgram Synthesis and VerificationML Assisted Software DevelopmentMultimodal Code UnderstandingMultimodal Learning, Vision and PerceptionComputer Vision and Visual RecognitionVision Language Models and Multimodal Fusion3D Vision, Scene Understanding and Embodied PerceptionAudio, Speech and Sensor Based LearningDifferentiable Programming and Implicit ModelsDifferentiable Optimization LayersImplicit Neural Representations and Equilibrium ModelsDifferentiable Physics and SimulationEnd to End Differentiable PipelinesAgentic AI and Autonomous ML SystemsAutonomous ML Agents and Tool Using SystemsMulti Agent Learning, Cooperation and NegotiationPlanning + Reasoning + Acting LoopsAgentic Evaluation and Safety FrameworksQuantum Machine LearningQuantum Inspired ML AlgorithmsHybrid Quantum Classical ModelsQuantum Optimization and SimulationML for Biology, Medicine and Synthetic Bio DesignProtein and Molecule Design with MLDNA/RNA Sequence ModelingML for Gene Editing and Synthetic BiologyBiological Foundation ModelsML for Economics, Markets and Mechanism DesignMarket Simulation and PredictionMechanism Design and AuctionsGame Theoretic Machine LearningML for Economic ForecastingML for Infrastructure, Networking and Systems OptimizationML for Cloud and Distributed SystemsML for Networking, Routing and Traffic OptimizationML for Resource Allocation and SchedulingGeospatial, Earth Observation and Climate MLSatellite Imagery and Remote Sensing MLGeospatial Forecasting and MappingClimate Modeling and Environmental MLData Mining, Knowledge Discovery and Predictive AnalyticsPattern Mining and Anomaly DetectionPredictive Modeling and ForecastingLarge Scale Data Mining and Big Data AnalyticsKnowledge Discovery in Databases (KDD)Applied Machine Learning Across DomainsHealthcare, Bioinformatics and Drug DiscoveryFinance, Economics and Risk ModelingCybersecurity and Threat DetectionSocial Media, BehaviorModeling and MisinformationEducation, Personalization and Learning AnalyticsIndustrial Systems, IoT and Smart ManufacturingEvaluation, Benchmarking and ReproducibilityML Evaluation Metrics and Benchmark DesignReproducibility, Transparency and Open ScienceDataset Governance, Quality and Bias DetectionModel Auditing and Performance DiagnosticsAI Governance, Ethics and Societal ImpactFairness, Bias and Ethical AIAI Governance, Regulation and Policy FrameworksSocietal Impact and Responsible DeploymentHuman Centered and Human AI Collaborative SystemsPaper SubmissionAuthors are invited to submit papers through the conferenceSubmission SystembyJune 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 byComputer Science Conference Proceedings(H index 46) inComputer Science & Information Technology (CS & IT)series (Confirmed).Selected papers fromMLT 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)Important DatesSubmission Deadline:June 06, 2026(Final Call)Authors Notification:June 15, 2026Final Manuscript Due:June 18, 2026