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Data Compression with Machine Learning
Karen Ullrich · Yibo Yang · Stephan Mandt

Mon Dec 05 02:00 PM -- 04:30 PM (PST) @ Virtual

The efficient communication of information has enormous societal and environmental impact, and stands to benefit from the machine learning revolution seen in other fields. Through this tutorial, we hope to disseminate the ideas of information theory and compression to a broad audience, overview the core methodologies in learning-based compression (i.e., neural compression), and present the relevant technical challenges and open problems defining a new frontier of probabilistic machine learning. Besides covering the technical grounds, we will also explore the broader underlying themes and future research in our panel discussion, focusing on the interplay between computation and communication, the role of machine learning, and societal considerations such as fairness, privacy, and energy footprint as we strive to make our learning and information processing systems more efficient.

Author Information

Karen Ullrich (Meta AI)

I am a research scientist (s/h) at FAIR NY and am actively collaborating with researchers from the Vector Institute and the UoAmsterdam. My main research focus lies in the intersection of information theory and probabilistic machine learning / deep learning 💜. I previously completed a PhD under the supervision of Prof. Max Welling. Prior to that, I worked at the Austrian Research Institute for AI, Intelligent Music Processing and Machine Learning Group lead by Prof. Gerhard Widmer. I studied Physics and Numerical Simulations in Leipzig and Amsterdam.

Yibo Yang (University of California, Irivine)
Stephan Mandt (University of California, Irvine)

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