IEEE Members: $11.00
Non-members: $15.00Pages/Slides: 53
This webinar will delve into the need for constraint-aware ML. We will present how to integrate key constrained optimization principles within the training process of deep learning models, endowing them with the capability of handling hard constraints and physical principles. The resulting models will bring a new level of accuracy and efficiency to hard decision tasks, which will be showcased on energy and scheduling problems. We will then introduce a powerful integration of constrained optimization as neural network layers, resulting in ML models that are able to enforce structure in the outputs of learned embeddings. This integration will provide ML models with enhanced expressiveness and modeling ability, which will be showcased through the certification fairness in learning to rank tasks and the assembly of high-quality ensemble models. Finally, we will discuss a number of grand challenges that I plan to address to develop a potentially transformative technology for both optimization and machine learning.