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Bridging Mathematical Optimization, Information Theory, and Data Science
14-16 May 2018
Workshop Organizer: Program Organizers: Yuxin Chen (EE), Mengdi Wang (ORFE)
Recent years have witnessed a flurry of exciting new developments and activities in the intersection of optimization theory, information theory, and mathematical data science. For instance, optimization theory inspires algorithmic breakthroughs in machine learning and reinforcement learning; information theory offers powerful tools for understanding the fundamental limits in numerous data science applications; and the growing popularity of data science and statistical learning in turn provides new data-driven perspectives to optimization paradigms and enriches the toolbox of information theory.
The goal of this workshop is to bring together participants from multiple communities including mathematical optimization, information theory, statistics, and machine learning in order to conduct in-depth discussion and motivate interdisciplinary collaboration.
This workshop is supported in part by Princeton Center for Statistics and Machine Learning (CSML); Department of Electrical Engineering; and Department of Operations Research and Financial Engineering (ORFE).
List of Speakers & Workshop Information
Registration is closed.
If you are already registered and would like to present a poster, please email the poster title and abstract to firstname.lastname@example.org no later than April 30.
Poster Session Instructions here.
The Meeting will be Live Streamed and can be viewed here.
Conference Hotel Information
Travel and Local Information