8th International Conference on Machine Learning & Applications (CMLA 2026)
Local: London, United Kingdom
Data do evento: 16/07/2026 a 17/07/2026
Prazo de submissão de trabalhos: 06/06/2026
8thInternational Conference on Machine Learning & Applications (CMLA 2026)July 16 ~ 17, 2026, London, United KingdomHybrid -- Registered authors can present their work online or face to face.Scope & TopicsThe8thInternational 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 include, but are not limited to, the followingFoundations of Machine LearningStatistical Learning Theory and GeneralizationOptimization for ML (Convex, Non Convex, Large Scale)Probabilistic Modeling, Bayesian Learning and Graphical ModelsCausal Inference, Causal ML and Counterfactual ReasoningOnline Learning, Meta Learning and Continual LearningMulti Task Learning, Transfer Learning and Domain AdaptationTheory of Deep Learning and Emergent BehaviorsDeep Learning and Representation LearningNeural Network Architectures and Training TechniquesSelf Supervised Learning and Contrastive LearningGenerative Models (GANs, Diffusion Models, VAEs)Diffusion Models for Images, Text, Time Series, Molecules and GraphsFoundation Models, LLMs, Vision Language Models and Multimodal ModelsEfficient Deep Learning (Pruning, Quantization, Distillation)Representation Learning for Structured, Temporal and Graph DataReinforcement Learning, Decision Making and Embodied AIDeep Reinforcement Learning and Policy OptimizationMulti Agent RL, Game Theory and CoordinationOffline RL, Safe RL and Risk Sensitive RLWorld Models, Embodied AI and Interactive LearningRL for Robotics, Control Systems and Real World DeploymentHierarchical RL and Skill DiscoveryPlanning Augmented Models and Decision TransformersNatural Language Processing, Speech and Multimodal AILarge Language Models and Instruction Tuned ModelsRetrieval Augmented Generation (RAG) and Knowledge Grounded ModelsLong Context Models, Memory Augmented Models and Tool Using LLMsText Generation, Summarization and Dialogue SystemsSpeech Recognition, Speech Synthesis and Audio Language ModelsVision Language Models, Video Language Models and Multimodal FusionNLP for Low Resource Languages and Cross Lingual LearningComputer Vision, Perception and GraphicsImage Classification, Detection and Segmentation3D Vision, Scene Understanding and SLAMVision Transformers and Diffusion Based Vision ModelsVideo Understanding, Action Recognition and Motion PredictionGenerative Vision Models, Neural Rendering and 3D GenerationEmbodied Perception and Interactive VisionVision Language Action Models for RoboticsData Mining, Knowledge Discovery and Graph LearningGraph Neural Networks (GNNs) and Graph Representation LearningKnowledge Graphs, Reasoning and Neuro Symbolic AILarge Scale Data Mining and Pattern DiscoveryTime Series Forecasting, Anomaly Detection and Predictive ModelingSimulation Based ML and Synthetic Data GenerationML for Structured, Relational and Heterogeneous DataTrustworthy, Explainable and Responsible AIExplainable AI (XAI) and Mechanistic InterpretabilityFairness, Accountability, Transparency and Ethics in MLRobust ML, Adversarial Attacks and DefensesJailbreak Resistant LLMs and Safety EvaluationPrivacy Preserving ML (Differential Privacy, Federated Learning, Secure ML)Safety Critical ML and ReliabilityAI Governance, Risk Assessment and Policy Aligned MLML Systems, Hardware Acceleration and Efficient ComputingDistributed and Parallel ML SystemsTraining and Inference Optimization for Foundation ModelsML Compilers, Optimization and Deployment FrameworksEdge ML, TinyML and On Device LearningEdge Native Foundation Models and Distributed InferenceNeuromorphic Computing and Brain Inspired MLEnergy Efficient ML, Green AI and Carbon Aware ML PipelinesApplied Machine Learning and Domain Specific MLHealthcare and Life SciencesMedical Imaging, Diagnostics and Clinical Decision SupportComputational Biology, Genomics and Drug DiscoveryDigital Health, Wearables and Personalized MedicineML for Neuroscience and Cognitive ModelingML for Digital Therapeutics and Clinical Decision AutomationScience and EngineeringML for Physics, Chemistry, Materials Science and Climate ModelingPhysics Informed ML and Scientific Machine Learning (SciML)Differentiable Physics, Neural Simulators and ML Accelerated SimulationML for Robotics, Autonomous Systems and ControlML for Smart Cities, IoT and Cyber Physical SystemsBusiness, Finance and Social SystemsML for Finance, Risk Modeling and Fraud DetectionRecommender Systems, Personalization and User ModelingSocial Network Analysis and Computational Social ScienceML for Policy Simulation and Societal Impact ModelingEmerging TrendsAgentic AI, Autonomous AI Systems and Multi Agent LLM EcosystemsTool Using AI, Planning Augmented LLMs and Autonomous AgentsProgram Synthesis, AI for Code and ML Guided Theorem ProvingQuantum Machine Learning and Quantum Inspired AlgorithmsAutoML, Neural Architecture Search (NAS) and Hyperparameter OptimizationML for Foundation Model Alignment, Safety and GovernanceML for Autonomous Scientific Discovery and Robot ScientistsML for Synthetic Biology, Bio Inspired Algorithms and Living SystemsPaper SubmissionAuthors are invited to submit papers through the conferenceSubmission SystembyMay 30, 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 byComputer Science Conference Proceedings(H index 46) inComputer Science & Information Technology (CS & IT)series (Confirmed).Selected papers fromCMLA 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)dvances in Vision Computing: An International Journal (AVC)Important DatesSubmission Deadline:June 06, 2026Authors Notification:July 06, 2026Final Manuscript Due:July 12, 2026