Optimal Learning to Rank

I am so pleased to have Dr. Charles Elkan give our next talk. Charles runs ML at Goldman Sachs, but the talk is in his capacity as faculty member at the University of California, San Diego.

Title: Optimal Learning to Rank

Bio: Charles Elkan is an adjunct professor of computer science at the University of California, San Diego (UCSD). He is currently Managing Director and Global Head of Machine Learning at Goldman Sachs. From 2014 to 2018 he was the first Amazon Fellow, leading a team of over 30 scientists and engineers in Seattle, Palo Alto, and New York doing research and development in applied machine learning in both e-commerce and cloud computing. Before joining Amazon, he was a tenured full professor of computer science at UCSD. His Ph.D. is from Cornell, in computer science and his undergraduate degree is from Cambridge, in mathematics.

You can find some of his published works here: https://scholar.google.com/citations?user=im5aMngAAAAJ&hl=en&oi=ao

Abstract:

Consider the scenario where an algorithm is given a context, and then it must select a slate of relevant results to display. As four special cases, the context may be a search query, an advertising slot, a social media user, or an opportunity to show recommendations. We want to compare many alternative ranking functions that select results in different ways. However, A/B testing with traffic from real users is expensive. This research provides a method to use traffic that was exposed to a past ranking function to obtain an unbiased estimate of the utility of a hypothetical new ranking function. The method is a purely offline computation, and relies on assumptions that are quite reasonable. We show further how to design a ranking function that is the best possible, given the same assumptions. Learning the best possible ranking of results given a search query is a special case. Experimental findings on real-world data logged by an e-commerce web site are positive.

Do not miss this talk! and feel free to ask him about his transition from academia to Amazon and then to Goldman Sachs at the Q&A.