Pratik Jawanpuria

According to our database1, Pratik Jawanpuria authored at least 59 papers between 2011 and 2026.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

Book  In proceedings  Article  PhD thesis  Dataset  Other 

Links

On csauthors.net:

Bibliography

2026
Nyström Approximation on Manifolds.
CoRR, May, 2026

LOFT: Low-Rank Orthogonal Fine-Tuning via Task-Aware Support Selection.
CoRR, May, 2026

Intrinsic Muon: Spectral Optimization on Riemannian Matrix Manifolds.
CoRR, May, 2026

Federated Learning on Riemannian Manifolds with Differential Privacy.
Mach. Learn., April, 2026

FedSEA: Achieving Benefit of Parallelization in Federated Online Learning.
CoRR, April, 2026

UniPROT: Uniform Prototype Selection via Partial Optimal Transport with Submodular Guarantees.
CoRR, April, 2026

2025
Generalized infinite dimensional Alpha-Procrustes based geometries.
CoRR, November, 2025

A Riemannian Approach to Ground Metric Learning for Optimal Transport.
Proceedings of the 2025 IEEE International Conference on Acoustics, 2025

2024
Riemannian block SPD coupling manifold and its application to optimal transport.
Mach. Learn., April, 2024

MMD-Regularized Unbalanced Optimal Transport.
Trans. Mach. Learn. Res., 2024

Revisiting stochastic submodular maximization with cardinality constraint: A bandit perspective.
Trans. Mach. Learn. Res., 2024

AutoDocSegmenter: A Geometric Approach towards Self-Supervised Document Segmentation.
Trans. Mach. Learn. Res., 2024

Differentially private Riemannian optimization.
Mach. Learn., 2024

Riemannian Federated Learning via Averaging Gradient Stream.
CoRR, 2024

A Framework for Bilevel Optimization on Riemannian Manifolds.
Proceedings of the Advances in Neural Information Processing Systems 37: Annual Conference on Neural Information Processing Systems 2024, 2024

SLTrain: a sparse plus low rank approach for parameter and memory efficient pretraining.
Proceedings of the Advances in Neural Information Processing Systems 37: Annual Conference on Neural Information Processing Systems 2024, 2024

A Gauss-Newton Approach for Min-Max Optimization in Generative Adversarial Networks.
Proceedings of the International Joint Conference on Neural Networks, 2024

Submodular framework for structured-sparse optimal transport.
Proceedings of the Forty-first International Conference on Machine Learning, 2024

Riemannian coordinate descent algorithms on matrix manifolds.
Proceedings of the Forty-first International Conference on Machine Learning, 2024

2023
Riemannian Hamiltonian Methods for Min-Max Optimization on Manifolds.
SIAM J. Optim., September, 2023

Improved Differentially Private Riemannian Optimization: Fast Sampling and Variance Reduction.
Trans. Mach. Learn. Res., 2023

Nonconvex-nonconcave min-max optimization on Riemannian manifolds.
Trans. Mach. Learn. Res., 2023

Light-weight Deep Extreme Multilabel Classification.
Proceedings of the International Joint Conference on Neural Networks, 2023

TBM-GAN: Synthetic Document Generation with Degraded Background.
Proceedings of the Document Analysis and Recognition - ICDAR 2023, 2023

Learning with Symmetric Positive Definite Matrices via Generalized Bures-Wasserstein Geometry.
Proceedings of the Geometric Science of Information - 6th International Conference, 2023

Riemannian Accelerated Gradient Methods via Extrapolation.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

Generative Pipeline for Data Augmentation of Unconstrained Document Images with Structural and Textural Degradation (Student Abstract).
Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

2022
Rieoptax: Riemannian Optimization in JAX.
CoRR, 2022

Confidence Score for Unsupervised Foreground Background Separation of Document Images.
CoRR, 2022

Generalised Spherical Text Embedding.
Proceedings of the 19th International Conference on Natural Language Processing, 2022

ProtoBandit: Efficient Prototype Selection via Multi-Armed Bandits.
Proceedings of the Asian Conference on Machine Learning, 2022

2021
Manifold optimization for optimal transport.
CoRR, 2021

SPOT: A Framework for Selection of Prototypes Using Optimal Transport.
Proceedings of the Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track, 2021

On Riemannian Optimization over Positive Definite Matrices with the Bures-Wasserstein Geometry.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Light-Weight Document Image Cleanup Using Perceptual Loss.
Proceedings of the 16th International Conference on Document Analysis and Recognition, 2021

Efficient Robust Optimal Transport with Application to Multi-Label Classification.
Proceedings of the 2021 60th IEEE Conference on Decision and Control (CDC), 2021

2020
Efficient robust optimal transport: formulations and algorithms.
CoRR, 2020

Learning Geometric Word Meta-Embeddings.
Proceedings of the 5th Workshop on Representation Learning for NLP, 2020

Statistical Optimal Transport posed as Learning Kernel Embedding.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

A Simple Approach to Learning Unsupervised Multilingual Embeddings.
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, 2020

Geometry-aware domain adaptation for unsupervised alignment of word embeddings.
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 2020

2019
Learning Multilingual Word Embeddings in Latent Metric Space: A Geometric Approach.
Trans. Assoc. Comput. Linguistics, 2019

A Riemannian gossip approach to subspace learning on Grassmann manifold.
Mach. Learn., 2019

Riemannian optimization on the simplex of positive definite matrices.
CoRR, 2019

Adaptive stochastic gradient algorithms on Riemannian manifolds.
CoRR, 2019

Riemannian adaptive stochastic gradient algorithms on matrix manifolds.
Proceedings of the 36th International Conference on Machine Learning, 2019

Low-rank approximations of hyperbolic embeddings.
Proceedings of the 58th IEEE Conference on Decision and Control, 2019

2018
McTorch, a manifold optimization library for deep learning.
CoRR, 2018

Low-rank geometric mean metric learning.
CoRR, 2018

A Dual Framework for Low-rank Tensor Completion.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

A Unified Framework for Structured Low-rank Matrix Learning.
Proceedings of the 35th International Conference on Machine Learning, 2018

2017
A Riemannian gossip approach to decentralized subspace learning on Grassmann manifold.
CoRR, 2017

A Saddle Point Approach to Structured Low-rank Matrix Learning in Large-scale Applications.
CoRR, 2017

2015
Generalized hierarchical kernel learning.
J. Mach. Learn. Res., 2015

Efficient Output Kernel Learning for Multiple Tasks.
Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

2014
On p-norm Path Following in Multiple Kernel Learning for Non-linear Feature Selection.
Proceedings of the 31th International Conference on Machine Learning, 2014

2012
A Convex Feature Learning Formulation for Latent Task Structure Discovery.
Proceedings of the 29th International Conference on Machine Learning, 2012

2011
Multi-task Multiple Kernel Learning.
Proceedings of the Eleventh SIAM International Conference on Data Mining, 2011

Efficient Rule Ensemble Learning using Hierarchical Kernels.
Proceedings of the 28th International Conference on Machine Learning, 2011


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