Parameter-free online optimization

Parameter-Free Online Optimization: Past, Present, and Future

Francesco Orabona, Associate Professor of Electrical and Computer Engineering, Boston University

Nov 14, 12:00 - 13:00

B9 L2 R2322

Parameter-free online optimization machine learning

Parameter-free online optimization is a class of algorithms that does not require tuning hyperparameters, yet they achieve the theoretical optimal performance. Moreover, they often achieve state-of-the-art performance too. An example would be gradient descent algorithms completely without learning rates. In this talk, I review my past and present contributions to this field. Building upon a fundamental idea connecting optimization, gambling, and information theory, I discuss selected applications of parameter-free algorithms to machine learning and statistics. Finally, we conclude with an overview of the future directions of this field.