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Computer Science Department Colloquium Limits of Memory-Efficient Learning: New Information-Theoretic Tools
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Speaker: Assistant Professor Sumegha Garg

Bio

Sumegha Garg is an Assistant Professor in the Department of Computer Science at Rutgers University. Prior to joining Rutgers, she was a postdoctoral fellow in the CS Department at Stanford University and a Rabin Postdoctoral Fellow in the Theory of Computation group at Harvard University. She completed her Ph.D. in Computer Science from Princeton University, where she was advised by Mark Braverman. Her research interests span complexity theory, information theory, and learning theory, with a particular emphasis on memory lower bounds and the theory of responsible machine learning.

Location : CoRE 301

Committee: 

Event Type: Computer Science Department Colloquium

Abstract: In this talk, I will discuss my work toward developing a foundational theory of memory-constrained machine learning (ML). While classical learning theory has extensively studied the data and time requirements of ML tasks, our understanding of their memory requirements remains limited. At the same time, the growing memory demands of large-scale ML systems, including large language models, make this question compelling both theoretically and practically. I will begin with an overview of my recent results quantifying memory requirements for a range of ML tasks, such as classification, mean estimation, and outlier detection. I will then introduce a new information-complexity–based framework for proving memory lower bounds in these settings. In the last part of the talk, I will briefly present my other recent results in complexity theory, particularly in memory-constrained property testing and coding theory.

Organization: 

Contact  Professor Ulrich Kremer

Join Zoom Meeting
https://rutgers.zoom.us/j/2014444359?pwd=WW9ybFNCNVFrUWlycHowSHdNZjhzUT09

Meeting ID: 201 444 4359
Password: 550978