Another Brick in the AI Wall: Building Practical Solutions from Theoretical Foundations

The shift towards foundation models has overshadowed the unique insights of deep learning theory, resulting in a loss of valuable knowledge and resources for the community. As machine learning and computer vision extend into new domains, such as biology, a deeper understanding of vision tasks becomes increasingly important. This workshop will provide a crucial platform for discussing the systematic challenges of integrating theory and practice. Concretely, to bridge the gap between theoretical research in machine learning and its practical applications, the workshop aims to explore how theoretical tools can be leveraged to perform rigorous worst-case analysis, crucial for deploying machine learning models in safety-critical societal domains like healthcare, education, and sustainability.