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There is now a CONTENT FREEZE for Mercury while we switch to a new platform. It began on Friday, March 10 at 6pm and will end on Wednesday, March 15 at noon. No new content can be created during this time, but all material in the system as of the beginning of the freeze will be migrated to the new platform, including users and groups. Functionally the new site is identical to the old one. webteam@gatech.edu
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Tess Malone, Communications Officer
Summary Sentence: Integrating Machine Learning into Algorithm Design
Full Summary: No summary paragraph submitted.
Ellen VitercikTITLE: Integrating Machine Learning into Algorithm Design
ABSTRACT:
An important property of those algorithms that are typically used in practice is broad applicability — the ability to solve problems across diverse domains. However, the default, out-of-the-box performance of these algorithms can be unsatisfactory with slow runtime, poor solution quality, and even negative long-term social ramifications. In practice, there is often ample data available about the types of problems an algorithm will be run on, data that can potentially be harnessed to fine-tune the algorithm’s performance. We therefore need principled approaches for using this data to obtain strong application-specific performance guarantees.
In this talk, I will give an overview of my research that provides practical methods built on firm theoretical foundations for incorporating machine learning and optimization into the process of algorithm design, selection, and configuration. I will describe my contributions across several diverse domains, including integer programming, clustering, mechanism design, and computational biology. As I will demonstrate, these seemingly disparate areas are connected by overarching structure which implies broadly-applicable guarantees.
BIO:
Ellen Vitercik is a Ph.D. student at Carnegie Mellon University, where she is co-advised by Maria-Florina Balcan and Tuomas Sandholm. Her research revolves around artificial intelligence, algorithm design, and the interface between economics and computation with a particular focus on machine learning theory. Among other honors, she is a recipient of the Exemplary Artificial Intelligence Track Paper Award at EC’19, the Best Presentation by a Student or Postdoctoral Researcher Award at EC’19, the NSF Graduate Research Fellowship, the IBM PhD Fellowship, the Fellowship in Digital Health from CMU’s Center for Machine Learning and Health, and the Teaching Assistant of the Year Award from CMU’s Machine Learning Department.
