Explore the intersection of statistics and computer science in machine learning through this conference talk by Garvesh Raskutti from UW-Madison. Delve into the world of fast and optimal low-rank tensor regression using importance sketching. Discover how the cross-fertilization between statistics and computer science has led to the development of modern machine learning paradigms. Learn about tensors, multi-way data, and higher-order solutions. Understand the challenges of low-rank tensor regression and the importance of tensor structure. Examine randomized sketching techniques and the ISLET algorithm for dimension-reduced regression. Gain insights into theoretical analysis, minimax lower bounds, and practical implementations. See comparisons with previous methods through simulations and explore real-world applications using an ADHD example. This 37-minute talk, presented at the Alan Turing Institute, offers a comprehensive overview of cutting-edge techniques in tensor regression and their implications for big data analysis. Read more