3 papers
cs.LG2026
Understanding Submodular Information Measure Based Objectives for Representation Learning: A Variance and Separation Perspective
Rishabh Iyer, Truong Pham, Anay Majee
Submodular Information Measures (SIMs) have recently emerged as a powerful framework for representation learning and multimodal learning. In particular, the SCORE framework~\cite{m…
cs.LG2026
SMA: Submodular Modality Aligner For Data Efficient Multimodal Learning
Truong Pham, Anay Majee, Rishabh Iyer
Despite the recent success of Multimodal Foundation Models (FMs), their reliance on massive paired datasets limits their applicability in low-data and rare-scenario settings where…
cs.LG2024
Theoretical Analysis of Submodular Information Measures for Targeted Data Subset Selection
Nathan Beck, Truong Pham, Rishabh Iyer
With increasing volume of data being used across machine learning tasks, the capability to target specific subsets of data becomes more important. To aid in this capability, the re…