23 citations · 129 across the 43 of their papers we have counts for
27 papers · 1 filter
A Riemannian Approach to Low-Rank Optimal Transport
Pratik Jawanpuria, Bamdev Mishra
Low-rank optimal transport (OT) mitigates the quadratic scaling of classical solvers, yet existing approaches rely heavily on first-order mirror-descent updates that require carefu…
Minibatch Selection for Language Models via Partition Matroid Constrained Gradient Matching
Prayas Agrawal, Prateek Chanda, Ishita Khatri +3
Training large language models (LLMs) on heterogeneous data requires selecting minibatches that balance convergence speed with coverage across domains. Existing methods either sele…
Riemannian Optimization for Hadamard Products of Low-Rank Matrices
Pratik Jawanpuria, Ankish Chandresh, Bamdev Mishra
The elementwise Hadamard product of two low-rank matrices provides a parameter-efficient model for data with multiplicative structure, but its modeling is challenging due to the pr…
LOFT: Low-Rank Orthogonal Fine-Tuning via Task-Aware Support Selection
Lanxin Zhao, Bamdev Mishra, Pratik Jawanpuria +4
Orthogonal parameter-efficient fine-tuning (PEFT) adapts pretrained weights through structure-preserving multiplicative transformations, but existing methods often conflate two dis…
Intrinsic Muon: Spectral Optimization on Riemannian Matrix Manifolds
Yibang Li, Bihari Lal Pandey, Ravi Sah +4
Muon and related norm-constrained matrix optimizers have become central to large-scale learning problems. They are formulated as a linear maximization oracle (LMO) over an ambient…
FedSEA: Achieving Benefit of Parallelization in Federated Online Learning
Harekrushna Sahu, Pratik Jawanpuria, Pranay Sharma
Online federated learning (OFL) has emerged as a popular framework for decentralized decision-making over continuous data streams without compromising client privacy. However, the…
UniPROT: Uniform Prototype Selection via Partial Optimal Transport with Submodular Guarantees
Prateek Chanda, Prayas Agrawal, Karthik S. Gurumoorthy +3
Selecting prototypical examples from a source distribution to represent a target data distribution is a fundamental problem in machine learning. Existing subset selection methods o…
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…
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…
ProtoBandit: Efficient Prototype Selection via Multi-Armed Bandits
Arghya Roy Chaudhuri, Pratik Jawanpuria, Bamdev Mishra
In this work, we propose a multi-armed bandit-based framework for identifying a compact set of informative data instances (i.e., the prototypes) from a source dataset that best…
SPOT: A framework for selection of prototypes using optimal transport
Karthik S. Gurumoorthy, Pratik Jawanpuria, Bamdev Mishra
In this work, we develop an optimal transport (OT) based framework to select informative prototypical examples that best represent a given target dataset. Summarizing a given targe…
Manifold optimization for non-linear optimal transport problems
Bamdev Mishra, N T V Satyadev, Hiroyuki Kasai +1
Optimal transport (OT) has recently found widespread interest in machine learning. It allows to define novel distances between probability measures, which have shown promise in sev…
MMD-Regularized Unbalanced Optimal Transport
Piyushi Manupriya, J. Saketha Nath, Pratik Jawanpuria
We study the unbalanced optimal transport (UOT) problem, where the marginal constraints are enforced using Maximum Mean Discrepancy (MMD) regularization. Our work is motivated by t…
Efficient Robust Optimal Transport with Application to Multi-Label Classification
Pratik Jawanpuria, N T V Satyadev, Bamdev Mishra
Optimal transport (OT) is a powerful geometric tool for comparing two distributions and has been employed in various machine learning applications. In this work, we propose a novel…
Geometry-aware Domain Adaptation for Unsupervised Alignment of Word Embeddings
Pratik Jawanpuria, Mayank Meghwanshi, Bamdev Mishra
We propose a novel manifold based geometric approach for learning unsupervised alignment of word embeddings between the source and the target languages. Our approach formulates the…
Statistical Optimal Transport posed as Learning Kernel Embedding
J. Saketha Nath, Pratik Jawanpuria
The objective in statistical Optimal Transport (OT) is to consistently estimate the optimal transport plan/map solely using samples from the given source and target marginal distri…
Low-rank approximations of hyperbolic embeddings
Pratik Jawanpuria, Mayank Meghwanshi, Bamdev Mishra
The hyperbolic manifold is a smooth manifold of negative constant curvature. While the hyperbolic manifold is well-studied in the literature, it has gained interest in the machine…
Riemannian adaptive stochastic gradient algorithms on matrix manifolds
Hiroyuki Kasai, Pratik Jawanpuria, Bamdev Mishra
Adaptive stochastic gradient algorithms in the Euclidean space have attracted much attention lately. Such explorations on Riemannian manifolds, on the other hand, are relatively ne…
Learning Multilingual Word Embeddings in Latent Metric Space: A Geometric Approach
Pratik Jawanpuria, Arjun Balgovind, Anoop Kunchukuttan +1
We propose a novel geometric approach for learning bilingual mappings given monolingual embeddings and a bilingual dictionary. Our approach decouples learning the transformation fr…
Low-rank geometric mean metric learning
Mukul Bhutani, Pratik Jawanpuria, Hiroyuki Kasai +1
We propose a low-rank approach to learning a Mahalanobis metric from data. Inspired by the recent geometric mean metric learning (GMML) algorithm, we propose a low-rank variant of…