Title: Online Convex Optimization
| Speaker: | Victor Sanches Portella |
| Affiliation: | University of British Columbia |
| Location: | MC 6029 |
Abstract: Online learning (OL) is a theoretical framework for learning with data online. Moreover, we usually make no assumptions on the distribution of the data, allowing it even to be adversarial to the learner. Maybe surprisingly, we can still design algorithms that, in some sense, “successfully learn” in this setting. This level of generality makes many of the ideas, algorithms, and techniques from OL useful in applications in theoretical computer science, optimization in machine learning, and control. In this talk I will give a brief introduction to the key concepts in online learning and mention a few topics within or adjacent to online learning that I believe cover fundamental ideas in OL and/or with interesting open research questions.