1 citations · 1 across the 4 of their papers we have counts for
4 papers
Leveraging Data Symmetries to Select an Optimal Subset of Training Data under Label Noise
Kumar Shubham, Pavan Karjol, Kiran M K +1
The performance of machine learning models often relies on large labeled datasets; however, data collected from diverse sources can contain label noise. Recent work has shown that,…
Spectral Discovery of Continuous Symmetries via Generalized Fourier Transforms
Pavan Karjol, Kumar Shubham, Prathosh AP
Continuous symmetries are fundamental to many scientific and learning problems, yet they are often unknown a priori. Existing symmetry discovery approaches typically search directl…
WISER: Weak supervISion and supErvised Representation learning to improve drug response prediction in cancer
Kumar Shubham, Aishwarya Jayagopal, Syed Mohammed Danish +2
Cancer, a leading cause of death globally, occurs due to genomic changes and manifests heterogeneously across patients. To advance research on personalized treatment strategies, th…
Fusing Conditional Submodular GAN and Programmatic Weak Supervision
Kumar Shubham, Pranav Sastry, Prathosh AP
Programmatic Weak Supervision (PWS) and generative models serve as crucial tools that enable researchers to maximize the utility of existing datasets without resorting to laborious…