International Conference on Optimization and Learning (OLA2025)

Our expert, Xavier Lessage, had the honor of presenting the latest advancements in secure federated learning, focusing on model compression and reverse distillation [1], at the International Conference on Optimization and Learning (OLA2025). This event was held at the Rochester Institute of Technology in Dubai from April 23 to 25, 2025. The conference focused on the future challenges of optimization and learning methods and their applications.

This conference provided the international research community in optimization and learning with a unique opportunity to exchange recent research findings and develop new ideas and collaborations in a convivial and relaxed atmosphere. The conference featured presentations covering various aspects of research in optimization and learning, such as:

• Optimization: Bayesian optimization, multi-objective optimization, global optimization, discrete optimization, mixed optimization, hybrid optimization, parallel optimization, simulation optimization, optimization under uncertainty, etc.
• Learning: supervised learning, unsupervised learning, deep models, neuromorphic models, federated learning, parallel/distributed learning, etc.
• Learning for Optimization: deep learning, large language models (LLM), reinforcement learning, tuning, algorithm selection, algorithm generation, etc.
  -* Optimization for Learning: neural architecture search, hyperparameter optimization, model selection, etc.
  -* Applications: quantum computing, Industry 4.0, biomedical, operations research, energy, etc.

See online: https://ola2025.sciencesconf.org/