Multi Index Model

Multi-index models represent a class of statistical models that aim to reduce the dimensionality of high-dimensional data by identifying low-dimensional latent structures that capture the essential information. Current research focuses on developing efficient algorithms, such as mean-field Langevin dynamics and approximate message-passing, to learn these low-dimensional representations within neural network architectures, particularly analyzing the interplay between sample complexity, computational complexity, and the underlying data distribution. This research is significant for advancing our theoretical understanding of neural network learning and for improving the efficiency and effectiveness of machine learning applications across diverse fields, including speech emotion recognition and ergonomics assessment.