Multi Objective Learning

Multi-objective learning (MOL) tackles the challenge of optimizing multiple, often conflicting, objectives simultaneously, aiming to find a set of optimal solutions representing different trade-offs. Current research focuses on developing efficient algorithms, such as variations of gradient descent and evolutionary methods, often integrated with deep learning architectures like transformers and graph convolutional networks, to address diverse applications. MOL's significance lies in its ability to improve model performance across various domains, from robotics and natural language processing to medical imaging and optimization problems, by enabling more nuanced and context-aware solutions. This approach is particularly valuable in safety-critical applications where balancing multiple objectives is crucial.