Frameworks for Efficient Algorithms for Learning: Robustness and Data Compression
Abhishek Shetty
IRB 4105 or https://umd.zoom.us/j/95853135696?pwd=VVEwMVpxeElXeEw0ckVlSWNOMVhXdz09
Tuesday, April 2, 2024, 11:00 am-12:00 pm 
Abstract
Though modern machine learning has been highly successful, as we move towards more critical applications, many challenges towards building trustworthy systems, such as ensuring robustness, privacy, and fairness, arise. Ad hoc and empirical approaches have often led to unintended consequences for these objectives, thus necessitating a principled approach. Traditional solutions often require redesigning entire pipelines or come with a significant loss in quality. In this talk, we will look at principles towards incorporating important desiderata into existing pipelines without significant computational and statistical overhead.
We will see two vignettes of this line of research. First, we introduce the smoothed adversary model for sequential decision making, which serves as a general model for learning under distribution shifts. In this setting, we will statistically and computationally efficient algorithms for decision making under uncertainty. Second, we will see a nearly linear-time algorithm for distribution compression leading to improved computational efficiency in diverse downstream statistical tasks.
Bio
Abhishek Shetty is currently a PhD student in the Department of Computer Science at the University of California at Berkeley advised by Nika Haghtalab. His research focuses on designing mathematical frameworks bringing together the theory and practice of machine learning and using these to develop simple algorithms with provable guarantees on real-world data. His research has been awarded a American Statistical Association SCSG best student paper award and the Apple AI/ML fellowship.
This talk is organized by Samuel Malede Zewdu