Our team is committed to advancing the scientific understanding of black-box optimization techniques and the pervasive Coupling Bias phenomena observed in contemporary AI systems, particularly deep neural networks. Through rigorous theoretical analysis and empirical investigation, we aim to shed light on the fundamental mechanisms underlying these prevalent challenges in the field of artificial intelligence.

• Black-Box Optimization: Exploring advanced methods to enhance the efficiency and effectiveness of optimization processes in complex, non-linear systems.
• Coupling Bias: Investigating the ubiquitous bias that arises from the interdependence of variables in AI models, with a focus on its impact on model performance and generalization.
• Deep Neural Networks: Delving into the intricacies of deep learning architectures to understand and mitigate the effects of coupling bias.

• Theoretical Analysis: Developing mathematical frameworks and models to elucidate the underlying principles of black-box optimization and coupling bias.
• Empirical Investigation: Conducting extensive experiments and case studies to validate theoretical findings and identify practical solutions.

• To provide a comprehensive understanding of the mechanisms driving black-box optimization and coupling bias in AI systems.
• To develop innovative strategies and tools that can effectively address these challenges and improve the robustness and reliability of AI applications.