Better Optimization Algorithms for Machine Learning | AI Talks

Machine Learning has been described as the fuel of the next industrial revolution. Yet, these machine algorithms are far from being automatic. From engineering ad-hoc normalization methods to manually tuning learning rates, we still heavily rely on humans to babysit the training process.

About the Speaker:
Francesco Orabona is an Associate Professor of Electrical & Computer Engineering at Boston University. His research interests lie in online learning, optimization, and statistical learning theory. He obtained his Ph.D. from the University of Genova in 2007. He previously was an Assistant Professor of Computer Science at Stony Brook University, a Senior Research Scientist at Yahoo Labs, and a Research Assistant Professor at the Toyota Technological Institute at Chicago. He received a Faculty Early Career Development (CAREER) from NSF in 2021 and a Google Research Award in 2017.