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Riemannian Preconditioning

SIAM journal on optimization, 2016-01, Vol.26 (1), p.635-660

2016

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Riemannian Stochastic Variance Reduced Gradient Algorithm with Retraction and Vector Transport

SIAM journal on optimization, 2019-01, Vol.29 (2), p.1444-1472

2019

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Riemannian block SPD coupling manifold and its application to optimal transport

Machine learning, 2024-04, Vol.113 (4), p.1595-1622

2024

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Differentially private Riemannian optimization

Machine learning, 2024-03, Vol.113 (3), p.1133-1161

2024

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A Riemannian gossip approach to subspace learning on Grassmann manifold

Machine learning, 2019-10, Vol.108 (10), p.1783-1803

2019

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Learning Multilingual Word Embeddings in Latent Metric Space: A Geometric Approach

Transactions of the Association for Computational Linguistics, 2019-01, Vol.7, p.107-120

2019

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Riemannian Hamiltonian Methods for Min-Max Optimization on Manifolds

SIAM journal on optimization, 2023-01, Vol.33 (3), p.1797-1827

2023

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Topological Interference Management With User Admission Control via Riemannian Optimization

IEEE transactions on wireless communications, 2017-11, Vol.16 (11), p.7362-7375

2017

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Fixed-rank matrix factorizations and Riemannian low-rank optimization

Computational statistics, 2014-06, Vol.29 (3-4), p.591-621

2014

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Heterogeneous Tensor Decomposition for Clustering via Manifold Optimization

IEEE transactions on pattern analysis and machine intelligence, 2016-03, Vol.38 (3), p.476-489

2016

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Low-Rank Optimization with Trace Norm Penalty

SIAM journal on optimization, 2013-01, Vol.23 (4), p.2124-2149

2013

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R3MC: A Riemannian three-factor algorithm for low-rank matrix completion

53rd IEEE Conference on Decision and Control, 2014, p.1137-1142

2014

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Riemannian joint dimensionality reduction and dictionary learning on symmetric positive definite manifolds

2018 26th European Signal Processing Conference (EUSIPCO), 2018, p.2010-2014

2018

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SPOT: A Framework for Selection of Prototypes Using Optimal Transport

Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track, 2021, Vol.12978, p.535-551

2021

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

Geometric Science of Information, 2023, Vol.14071, p.405-415

2023

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A Unified Framework for Domain Adaptation Using Metric Learning on Manifolds

Machine Learning and Knowledge Discovery in Databases, 2019, Vol.11052, p.843-860

2019

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A Sparse and Low-Rank Optimization Framework for Network Topology Control in Dense Fog-RAN

2017 IEEE 85th Vehicular Technology Conference (VTC Spring), 2017, p.1-5

2017

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A Riemannian Approach to Large-Scale Constrained Least-Squares with Symmetries

Mishra, Bamdev

2014

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

2024 International Joint Conference on Neural Networks (IJCNN), 2024, p.1-9

2024

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Light-weight Deep Extreme Multilabel Classification

2023 International Joint Conference on Neural Networks (IJCNN), 2023, p.1-8

2023

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Inductive Framework for Multi-Aspect Streaming Tensor Completion with Side Information

Proceedings of the 27th ACM International Conference on Information and Knowledge Management, 2018, p.307-316

2018

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Low-rank approximations of hyperbolic embeddings

2019 IEEE 58th Conference on Decision and Control (CDC), 2019, p.7159-7164

2019

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Detection of Review Abuse via Semi-Supervised Binary Multi-Target Tensor Decomposition

Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2019, p.2134-2144

2019

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Scaled stochastic gradient descent for low-rank matrix completion

2016 IEEE 55th Conference on Decision and Control (CDC), 2016, p.2820-2825

2016

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Sparse plus low-rank autoregressive identification in neuroimaging time series

2015 54th IEEE Conference on Decision and Control (CDC), 2015, p.3965-3970

2015

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Riemannian Preconditioning

Riemannian Stochastic Variance Reduced Gradient Algorithm with Retraction and Vector Transport

Riemannian block SPD coupling manifold and its application to optimal transport

Differentially private Riemannian optimization

A Riemannian gossip approach to subspace learning on Grassmann manifold

Learning Multilingual Word Embeddings in Latent Metric Space: A Geometric Approach

Riemannian Hamiltonian Methods for Min-Max Optimization on Manifolds

Topological Interference Management With User Admission Control via Riemannian Optimization

Fixed-rank matrix factorizations and Riemannian low-rank optimization

Heterogeneous Tensor Decomposition for Clustering via Manifold Optimization

Low-Rank Optimization with Trace Norm Penalty

R3MC: A Riemannian three-factor algorithm for low-rank matrix completion

Riemannian joint dimensionality reduction and dictionary learning on symmetric positive definite manifolds

SPOT: A Framework for Selection of Prototypes Using Optimal Transport

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

A Unified Framework for Domain Adaptation Using Metric Learning on Manifolds

A Sparse and Low-Rank Optimization Framework for Network Topology Control in Dense Fog-RAN

A Riemannian Approach to Large-Scale Constrained Least-Squares with Symmetries

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

Light-weight Deep Extreme Multilabel Classification

Inductive Framework for Multi-Aspect Streaming Tensor Completion with Side Information

Low-rank approximations of hyperbolic embeddings

Detection of Review Abuse via Semi-Supervised Binary Multi-Target Tensor Decomposition

Scaled stochastic gradient descent for low-rank matrix completion

Sparse plus low-rank autoregressive identification in neuroimaging time series

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