Pratik Jawanpuria

Trans. Mach. Learn. Res., 2024

Revisiting stochastic submodular maximization with cardinality constraint: A bandit perspective.

[BibT_eX]

[DOI]

Karthik S. Gurumoorthy

Trans. Mach. Learn. Res., 2024

AutoDocSegmenter: A Geometric Approach towards Self-Supervised Document Segmentation.

[BibT_eX]

[DOI]

Trans. Mach. Learn. Res., 2024

Differentially private Riemannian optimization.

[BibT_eX]

[DOI]

Mach. Learn., 2024

Riemannian Federated Learning via Averaging Gradient Stream.

[BibT_eX]

[DOI]

CoRR, 2024

Federated Learning on Riemannian Manifolds with Differential Privacy.

[BibT_eX]

[DOI]

CoRR, 2024

A Framework for Bilevel Optimization on Riemannian Manifolds.

[BibT_eX]

[DOI]

Andi Han

Akiko Takeda

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

SLTrain: a sparse plus low rank approach for parameter and memory efficient pretraining.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

A Gauss-Newton Approach for Min-Max Optimization in Generative Adversarial Networks.

[BibT_eX]

[DOI]

Proceedings of the International Joint Conference on Neural Networks, 2024

Submodular framework for structured-sparse optimal transport.

[BibT_eX]

[DOI]

Piyushi Manupriya

Karthik S. Gurumoorthy

Saketha Nath Jagarlapudi

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Riemannian coordinate descent algorithms on matrix manifolds.

[BibT_eX]

[DOI]

Andi Han

Proceedings of the Forty-first International Conference on Machine Learning, 2024

2023

Riemannian Hamiltonian Methods for Min-Max Optimization on Manifolds.

[BibT_eX]

[DOI]

SIAM J. Optim., September, 2023

Improved Differentially Private Riemannian Optimization: Fast Sampling and Variance Reduction.

[BibT_eX]

[DOI]

Trans. Mach. Learn. Res., 2023

Nonconvex-nonconcave min-max optimization on Riemannian manifolds.

[BibT_eX]

[DOI]

Trans. Mach. Learn. Res., 2023

Light-weight Deep Extreme Multilabel Classification.

[BibT_eX]

[DOI]

Proceedings of the International Joint Conference on Neural Networks, 2023

TBM-GAN: Synthetic Document Generation with Degraded Background.

[BibT_eX]

[DOI]

Proceedings of the Document Analysis and Recognition - ICDAR 2023, 2023

Learning with Symmetric Positive Definite Matrices via Generalized Bures-Wasserstein Geometry.

[BibT_eX]

[DOI]

Proceedings of the Geometric Science of Information - 6th International Conference, 2023

Riemannian Accelerated Gradient Methods via Extrapolation.

[BibT_eX]

[DOI]

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).

[BibT_eX]

[DOI]

Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

2022

Rieoptax: Riemannian Optimization in JAX.

[BibT_eX]

[DOI]

CoRR, 2022

Confidence Score for Unsupervised Foreground Background Separation of Document Images.

[BibT_eX]

[DOI]

Soumyadeep Dey

CoRR, 2022

Generalised Spherical Text Embedding.

[BibT_eX]

[DOI]

Proceedings of the 19th International Conference on Natural Language Processing, 2022

ProtoBandit: Efficient Prototype Selection via Multi-Armed Bandits.

[BibT_eX]

[DOI]

Arghya Roy Chaudhuri

Proceedings of the Asian Conference on Machine Learning, 2022

2021

Manifold optimization for optimal transport.

[BibT_eX]

[DOI]

CoRR, 2021

SPOT: A Framework for Selection of Prototypes Using Optimal Transport.

[BibT_eX]

[DOI]

Karthik S. Gurumoorthy

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.

[BibT_eX]

[DOI]

Andi Han

Junbin Gao

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.

[BibT_eX]

[DOI]

Soumyadeep Dey

Proceedings of the 16th International Conference on Document Analysis and Recognition, 2021

Efficient Robust Optimal Transport with Application to Multi-Label Classification.

[BibT_eX]

[DOI]

N. T. V. Satya Dev

Proceedings of the 2021 60th IEEE Conference on Decision and Control (CDC), 2021

2020

Efficient robust optimal transport: formulations and algorithms.

[BibT_eX]

[DOI]

N. T. V. Satya Dev

CoRR, 2020

Learning Geometric Word Meta-Embeddings.

[BibT_eX]

[DOI]

Proceedings of the 5th Workshop on Representation Learning for NLP, 2020

Statistical Optimal Transport posed as Learning Kernel Embedding.

[BibT_eX]

[DOI]

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.

[BibT_eX]

[DOI]

Mayank Meghwanshi

Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, 2020

Geometry-aware domain adaptation for unsupervised alignment of word embeddings.

[BibT_eX]

[DOI]

Mayank Meghwanshi

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.

[BibT_eX]

[DOI]

Trans. Assoc. Comput. Linguistics, 2019

A Riemannian gossip approach to subspace learning on Grassmann manifold.

[BibT_eX]

[DOI]

Mach. Learn., 2019

Riemannian optimization on the simplex of positive definite matrices.

[BibT_eX]

[DOI]

Hiroyuki Kasai

CoRR, 2019

Adaptive stochastic gradient algorithms on Riemannian manifolds.

[BibT_eX]

[DOI]

Hiroyuki Kasai

CoRR, 2019

Riemannian adaptive stochastic gradient algorithms on matrix manifolds.

[BibT_eX]

[DOI]

Hiroyuki Kasai

Proceedings of the 36th International Conference on Machine Learning, 2019

Low-rank approximations of hyperbolic embeddings.

[BibT_eX]

[DOI]

Mayank Meghwanshi

Proceedings of the 58th IEEE Conference on Decision and Control, 2019

2018

McTorch, a manifold optimization library for deep learning.

[BibT_eX]

[DOI]

CoRR, 2018

Low-rank geometric mean metric learning.

[BibT_eX]

[DOI]

CoRR, 2018

A Dual Framework for Low-rank Tensor Completion.

[BibT_eX]

[DOI]

Madhav Nimishakavi

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.

[BibT_eX]

[DOI]

Proceedings of the 35th International Conference on Machine Learning, 2018

2017

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

[BibT_eX]

[DOI]

CoRR, 2017

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

[BibT_eX]

[DOI]

CoRR, 2017

2015

Generalized hierarchical kernel learning.

[BibT_eX]

[DOI]

Ganesh Ramakrishnan

J. Mach. Learn. Res., 2015

Efficient Output Kernel Learning for Multiple Tasks.

[BibT_eX]

[DOI]

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.

[BibT_eX]

[DOI]

Manik Varma

J. Saketha Nath

Proceedings of the 31th International Conference on Machine Learning, 2014

2012

A Convex Feature Learning Formulation for Latent Task Structure Discovery.

[BibT_eX]

[DOI]

J. Saketha Nath

Proceedings of the 29th International Conference on Machine Learning, 2012

2011

Multi-task Multiple Kernel Learning.

[BibT_eX]

[DOI]

Proceedings of the Eleventh SIAM International Conference on Data Mining, 2011

Efficient Rule Ensemble Learning using Hierarchical Kernels.

[BibT_eX]

[DOI]