Abstract: Online learning is the study of decision-making with streaming data (e.g. repeated prediction problems). Classical results in this field have established tight upper and lower bounds for what can be achieved in the worst-case scenario, but leave open significant room for beyond-worst-case analysis. In this talk, we will describe recent results in a setting in which our learning algorithm has access to some external "hints" about the future of the stream. We will show how to lever
Bio: Ashok Cutkosky is an assistant professor in the ECE department at Boston University. Previously, he was a research scientist at Google. He earned a PhD in computer science from Stanford University in 2018. He is interested in all aspects of machine learning and stochastic optimization theory. He has worked extensively on optimization algorithms for machine learning that adaptively tune themselves to apriori unknown the statistical properties of their input datasets, as well as on non-convex stochastic optimization.
The Data Science Deep Dive is free and open to the public. If you are interested in giving a talk, please email Mohammad Hajiesmaili or Adam Lechowicz. Note that in addition to being a public lecture series, the Data Science Deep Dive is also a seminar (CompSci 692K, Algorithms with Predictions Seminar) that can be taken for credit.