7th International Conference on Advanced Machine Learning (AMLA 2026)

Name: 7th International Conference on Advanced Machine Learning (AMLA 2026)
Start: 2026-07-25T09:00:00-04:00
End: 2026-07-25T10:00:00-04:00
Location: Toronto, Canada

Schedule

Sat Jul 25 2026 at 09:00 am to 10:00 am

UTC-04:00

Location

Toronto, Canada | Toronto, ON

7th International Conference on Advanced Machine Learning (AMLA 2026)

July 25 ~ 26, 2026, Toronto, Canada

https://ais2026.org/amla/index

Scope

The 7th International 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 following

Machine Learning Foundations

Machine Learning Algorithms and Theory
Supervised, Unsupervised and Semi Supervised Learning
Learning in Knowledge Intensive Systems
Optimization, Generalization and Learning Dynamics
Probabilistic Modeling, Bayesian Learning and Uncertainty Quantification
Classical ML Tasks: Classification, Regression, Clustering, Ranking

Deep Learning and Representation Learning

Deep Neural Networks and Advanced Architectures
Self Supervised, Contrastive and Representation Learning
Foundation Models and Large Scale Pretraining
Parameter Efficient Fine Tuning (PEFT, LoRA, Adapters)
Multimodal Deep Learning (Vision, Text, Audio, Graphs)
Efficient Deep Learning: Distillation, Quantization, Pruning and Sparse Models
Scaling Laws and Training Dynamics of Large Models

Generative AI and Creative ML

Diffusion Models and Score Based Generative Models
Generative Transformers and Autoregressive Models
GANs and Hybrid Generative Architectures
Text to X, Image to X and Multimodal Generation
Synthetic Data Generation, Evaluation and Bias Control
Generative Agents and Simulation Driven Generation

Reinforcement Learning and Decision Making

Reinforcement Learning (RL) and Deep RL
RLHF (Reinforcement Learning from Human Feedback)
Model Based RL, World Models and Planning
Multi Agent RL and Game Theoretic Learning
RL for Robotics, Control, Games and Autonomous Systems
Causal RL and Safe RL

Agentic ML and Autonomous Learning Systems

Autonomous ML Agents and Tool Using Agents
Multi Agent Collaboration, Communication and Coordination
Planning Augmented ML Models
Agent Memory, Long Horizon Reasoning and Task Decomposition
Evaluation of Agentic Systems

Graph Machine Learning and Structured Models

Graph Neural Networks (GNNs)
Graph Transformers and Relational Learning
Knowledge Graph Embeddings and Reasoning
Structured Prediction and Probabilistic Graphical Models
Spatio Temporal Graph Learning

Causal ML, Reasoning and Explainability

Causal Inference and Causal Representation Learning
Counterfactual Reasoning and Causal Discovery
Causal Generative Modeling
Explainable ML (XAI) and Interpretable Models
Trustworthy ML: Robustness, Fairness and Bias Mitigation

Multimodal ML and Cross Domain Learning

Vision Language, Audio Language and Multimodal Transformers
Cross Modal Alignment, Fusion and Retrieval
Multimodal Representation Learning
Vision Language Action Models and Embodied ML

Time Series ML, Forecasting and Sequential Models

Temporal Transformers and Sequence Modeling
Forecasting, Predictive Modeling and Anomaly Detection
Sequential Decision Making and Temporal Representation Learning
ML for Sensor Data, IoT and Real Time Systems Optimization, ML Systems and Infrastructure
Optimization Algorithms for ML
Distributed Training, Parallel ML and Large Scale Systems
ML Compilers, Accelerators and Hardware Aware ML
Efficient Inference, Model Compression and Deployment
MLOps, ML Pipelines and Lifecycle Management
Memory Augmented ML and Long Context Models

Federated, Distributed and Privacy Preserving ML

Federated Learning and Collaborative ML
Differential Privacy and Secure ML
Edge ML, TinyML and On Device Intelligence
Privacy Preserving Training and Inference

Adversarial ML and ML Security

Adversarial Attacks and Defenses
Robust ML and Certified Robustness
Secure ML Pipelines and Model Integrity
Red Teaming ML Systems and Safety Critical ML

Meta Learning, Active Learning and Learning to Learn

Meta Learning and Few Shot Learning
Active Learning and Curriculum Learning
AutoML, Neural Architecture Search (NAS)
Continual Learning, Lifelong Learning and Catastrophic Forgetting Mitigation

Applied Machine Learning and Real World Systems

ML for Healthcare, Bioinformatics and Genomics
ML for Finance, Economics and Risk Modeling
ML for Engineering, Manufacturing and Industry 4.0
ML for Climate Science, Energy and Sustainability
ML for Social Computing, Recommendation and Personalization
ML for Scientific Discovery, Simulation and Physical Modeling
ML for Software Engineering, Code Generation and Program Synthesis

Paper Submission

Authors are invited to submit papers through the conference Submission System by May 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 by Computer Science Conference Proceedings in Computer Science & Information Technology (CS & IT) series (Confirmed).

Selected papers from AMLA 2026, after further revisions, will be published in the special issue of the following journal.