Parthe Pandit

Assistant Professor

IIT Bombay

Talk Title: Old dog, Old tricks, New show: Fast 1st order methods for training Kernel Machines.

Abstract:Kernel Machines are a classical family of models in Machine Learning that overcome several limitations of Neural Networks. These models have regained popularity following some landmark results showing their equivalence to Neural Networks. We propose a state of the art algorithm - EigenPro - based on gradient descent in the RKHS. This algorithm is much faster and requires less memory compared to previous attempts, and enables training large scale Kernel Machines over large datasets.

Bio:Parthe Pandit is a Thakur Family Chair Assistant Professor at the Center for Machine Intelligence and Data Science (C-MInDS) at IIT Bombay. He was a Simons postdoctoral fellow at UC San Diego. He obtained his PhD from UCLA, and his undergraduate education from IIT Bombay. He has received the AI2050 Early Career Fellowship from Schmidt Sciences in 2024, and the Jack K Wolf Student Paper Award at ISIT 2019.