1 citations · 2 across the 17 of their papers we have counts for
5 papers · 2 filters
A Riemannian Approach to Ground Metric Learning for Optimal Transport
Pratik Jawanpuria, Dai Shi, Bamdev Mishra +1
Optimal transport (OT) theory has attracted much attention in machine learning and signal processing applications. OT defines a notion of distance between probability distributions…
Riemannian Federated Learning via Averaging Gradient Streams
Zhenwei Huang, Wen Huang, Pratik Jawanpuria +1
Federated learning (FL) as a distributed learning paradigm has a significant advantage in addressing large-scale machine learning tasks. In the Euclidean setting, FL algorithms hav…
Submodular Framework for Structured-Sparse Optimal Transport
Piyushi Manupriya, Pratik Jawanpuria, Karthik S. Gurumoorthy +2
Unbalanced optimal transport (UOT) has recently gained much attention due to its flexible framework for handling un-normalized measures and its robustness properties. In this work,…
SLTrain: a sparse plus low-rank approach for parameter and memory efficient pretraining
Andi Han, Jiaxiang Li, Wei Huang +4
Large language models (LLMs) have shown impressive capabilities across various tasks. However, training LLMs from scratch requires significant computational power and extensive mem…
A Gauss-Newton Approach for Min-Max Optimization in Generative Adversarial Networks
Neel Mishra, Bamdev Mishra, Pratik Jawanpuria +1
A novel first-order method is proposed for training generative adversarial networks (GANs). It modifies the Gauss-Newton method to approximate the min-max Hessian and uses the Sher…