Optimal Model

Optimal model research focuses on identifying and selecting the best-performing machine learning model for a given task, considering various constraints and objectives. Current research emphasizes finding models that balance accuracy with fairness, privacy, interpretability, and computational efficiency, often employing techniques like quantization, structured matrices (e.g., Block Tensor-Train), and evolutionary algorithms. This work is crucial for developing reliable, responsible, and efficient AI systems across diverse applications, ranging from financial modeling to medical diagnosis and beyond, by addressing the trade-offs inherent in model selection.