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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…
Riemannian coordinate descent algorithms on matrix manifolds
Andi Han, Pratik Jawanpuria, Bamdev Mishra
Many machine learning applications are naturally formulated as optimization problems on Riemannian manifolds. The main idea behind Riemannian optimization is to maintain the feasib…
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…
Federated Learning on Riemannian Manifolds with Differential Privacy
Zhenwei Huang, Wen Huang, Pratik Jawanpuria +1
In recent years, federated learning (FL) has emerged as a prominent paradigm in distributed machine learning. Despite the partial safeguarding of agents' information within FL syst…
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…
A Framework for Bilevel Optimization on Riemannian Manifolds
Andi Han, Bamdev Mishra, Pratik Jawanpuria +1
Bilevel optimization has gained prominence in various applications. In this study, we introduce a framework for solving bilevel optimization problems, where the variables in both t…