7th International Conference on Advanced Machine Learning (AMLA 2026)
Local: Toronto, Canada
Data do evento: 25/07/2026 a 26/07/2026
Prazo de submissão de trabalhos: 06/06/2026
7thInternational Conference on Advanced Machine Learning (AMLA 2026)July 25 ~ 26, 2026, Toronto, CanadaHybrid -- Registered authors can present their work online or face to face.Scope & Topics7thInternational Conference on Advanced Machine Learning (AMLA 2026)serves as a premier international forum for presenting cutting edge research, exchanging ideas, and exploring the latest breakthroughs in Machine Learning and its rapidly expanding ecosystem. As ML continues to transform science, engineering, industry, and society, AMLA 2026 aims to highlight both foundational advances and emerging innovations that define the next generation of intelligent systems.Topics of interest include, but are not limited to, the followingMachine Learning FoundationsMachine Learning Algorithms and TheorySupervised, Unsupervised and Semi Supervised LearningLearning in Knowledge Intensive SystemsOptimization, Generalization and Learning DynamicsProbabilistic Modeling, Bayesian Learning and Uncertainty QuantificationClassical ML Tasks: Classification, Regression, Clustering, RankingDeep Learning and Representation LearningDeep Neural Networks and Advanced ArchitecturesSelf Supervised, Contrastive and Representation LearningFoundation Models and Large Scale PretrainingParameter Efficient Fine Tuning (PEFT, LoRA, Adapters)Multimodal Deep Learning (Vision, Text, Audio, Graphs)Efficient Deep Learning: Distillation, Quantization, Pruning and Sparse ModelsScaling Laws and Training Dynamics of Large ModelsGenerative AI and Creative MLDiffusion Models and Score Based Generative ModelsGenerative Transformers and Autoregressive ModelsGANs and Hybrid Generative ArchitecturesText to X, Image to X and Multimodal GenerationSynthetic Data Generation, Evaluation and Bias ControlGenerative Agents and Simulation Driven GenerationReinforcement Learning and Decision MakingReinforcement Learning (RL) and Deep RLRLHF (Reinforcement Learning from Human Feedback)Model Based RL, World Models and PlanningMulti Agent RL and Game Theoretic LearningRL for Robotics, Control, Games and Autonomous SystemsCausal RL and Safe RLAgentic ML and Autonomous Learning SystemsAutonomous ML Agents and Tool Using AgentsMulti Agent Collaboration, Communication and CoordinationPlanning Augmented ML ModelsAgent Memory, Long Horizon Reasoning and Task DecompositionEvaluation of Agentic SystemsGraph Machine Learning and Structured ModelsGraph Neural Networks (GNNs)Graph Transformers and Relational LearningKnowledge Graph Embeddings and ReasoningStructured Prediction and Probabilistic Graphical ModelsSpatio Temporal Graph LearningCausal ML, Reasoning and ExplainabilityCausal Inference and Causal Representation LearningCounterfactual Reasoning and Causal DiscoveryCausal Generative ModelingExplainable ML (XAI) and Interpretable ModelsTrustworthy ML: Robustness, Fairness and Bias MitigationMultimodal ML and Cross Domain LearningVision Language, Audio Language and Multimodal TransformersCross Modal Alignment, Fusion and RetrievalMultimodal Representation LearningVision Language Action Models and Embodied MLTime Series ML, Forecasting and Sequential ModelsTemporal Transformers and Sequence ModelingForecasting, Predictive Modeling and Anomaly DetectionSequential Decision Making and Temporal Representation LearningML for Sensor Data, IoT and Real Time Systems Optimization, ML Systems and InfrastructureOptimization Algorithms for MLDistributed Training, Parallel ML and Large Scale SystemsML Compilers, Accelerators and Hardware Aware MLEfficient Inference, Model Compression and DeploymentMLOps, ML Pipelines and Lifecycle ManagementMemory Augmented ML and Long Context ModelsFederated, Distributed and Privacy Preserving MLFederated Learning and Collaborative MLDifferential Privacy and Secure MLEdge ML, TinyML and On Device IntelligencePrivacy Preserving Training and InferenceAdversarial ML and ML SecurityAdversarial Attacks and DefensesRobust ML and Certified RobustnessSecure ML Pipelines and Model IntegrityRed Teaming ML Systems and Safety Critical MLMeta Learning, Active Learning and Learning to LearnMeta Learning and Few Shot LearningActive Learning and Curriculum LearningAutoML, Neural Architecture Search (NAS)Continual Learning, Lifelong Learning and Catastrophic Forgetting MitigationApplied Machine Learning and Real World SystemsML for Healthcare, Bioinformatics and GenomicsML for Finance, Economics and Risk ModelingML for Engineering, Manufacturing and Industry 4.0ML for Climate Science, Energy and SustainabilityML for Social Computing, Recommendation and PersonalizationML for Scientific Discovery, Simulation and Physical ModelingML for Software Engineering, Code Generation and Program SynthesisPaper SubmissionAuthors are invited to submit papers through the conferenceSubmission SystembyJune 06, 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 The proceedings of the conference will be published byComputer Science Conference Proceedings(H index 43) inComputer Science & Information Technology (CS & IT)series (Confirmed).Selected papers fromAMLA 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, 2026Authors Notification:July 12, 2026Final Manuscript Due:July 18, 2026