7 citations · 7 across the 6 of their papers we have counts for
6 papers
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…
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…
Light-weight Deep Extreme Multilabel Classification
Istasis Mishra, Arpan Dasgupta, Pratik Jawanpuria +2
Extreme multi-label (XML) classification refers to the task of supervised multi-label learning that involves a large number of labels. Hence, scalability of the classifier with inc…
Riemannian accelerated gradient methods via extrapolation
Andi Han, Bamdev Mishra, Pratik Jawanpuria +1
In this paper, we propose a simple acceleration scheme for Riemannian gradient methods by extrapolating iterates on manifolds. We show when the iterates are generated from Riemanni…
A Convex Feature Learning Formulation for Latent Task Structure Discovery
Pratik Jawanpuria, J. Saketha Nath
This paper considers the multi-task learning problem and in the setting where some relevant features could be shared across few related tasks. Most of the existing methods assume t…