In the face of the data onslaught, smart algorithms have a significant role to play. Over the last couple of decades, coresets, a small and efficiently calculable data summary, have grown in popularity, both in theoretical and practical settings. They enable approximating large optimizations while needing only a fraction of the resources. In this talk, we will discuss a few recent results related to creating coresets for two related problems ----(symmetric) tensor factorization and Lp subspace embedding. Our coresets for tensor factorization are online in nature, i.e., for every incoming point, it takes an irrevocable decision whether to include it in the coreset.
For Lp subspace embedding we also present a deterministic coreset by generalizing the work of Batson, Spielman, Srivastava (BSS) and extending it from L2 to Lp.
Short Bio: Anirban Dasgupta is currently the N. Rama Rao Chair Professor of Computer Science & Engineering at IIT Gandhinagar. Prior to being at IIT Gandhinagar, he was a Senior Scientist at Yahoo Labs Sunnyvale. Anirban works on algorithmic problems for massive data sets, large-scale machine learning, analysis of large social networks, and randomized algorithms in general. He did his undergraduate studies at IIT Kharagpur and doctoral studies at Cornell University. He has received the Google Faculty Research Award (2015), the Cisco University Award (2016), the ICDT Best Newcomer Award (2016), the Google India AI/ML Award (2020), and the ACM STOC 10 year Test of Time award in 2024.
