Search Results for author: Bamdev Mishra

Found 40 papers, 15 papers with code

Riemannian Hamiltonian methods for min-max optimization on manifolds

no code implementations25 Apr 2022 Andi Han, Bamdev Mishra, Pratik Jawanpuria, Pawan Kumar, Junbin Gao

In this paper, we study the min-max optimization problems on Riemannian manifolds.

Generalized Bures-Wasserstein Geometry for Positive Definite Matrices

1 code implementation20 Oct 2021 Andi Han, Bamdev Mishra, Pratik Jawanpuria, Junbin Gao

This paper proposes a generalized Bures-Wasserstein (BW) Riemannian geometry for the manifold of symmetric positive definite matrices.

On Riemannian Optimization over Positive Definite Matrices with the Bures-Wasserstein Geometry

1 code implementation NeurIPS 2021 Andi Han, Bamdev Mishra, Pratik Jawanpuria, Junbin Gao

We build on this to show that the BW metric is a more suitable and robust choice for several Riemannian optimization problems over ill-conditioned SPD matrices.

Riemannian optimization

SPOT: A framework for selection of prototypes using optimal transport

no code implementations18 Mar 2021 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.

Decision Making Prototype Selection

Manifold optimization for non-linear optimal transport problems

1 code implementation1 Mar 2021 Bamdev Mishra, N T V Satyadev, Hiroyuki Kasai, Pratik Jawanpuria

In this work, we discuss how to computationally approach general non-linear OT problems within the framework of Riemannian manifold optimization.

Efficient Robust Optimal Transport with Application to Multi-Label Classification

2 code implementations22 Oct 2020 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.

Multi-class Classification Multi-Label Classification +1

Learning Geometric Word Meta-Embeddings

no code implementations WS 2020 Pratik Jawanpuria, N T V Satya Dev, Anoop Kunchukuttan, Bamdev Mishra

We propose a geometric framework for learning meta-embeddings of words from different embedding sources.

Word Similarity

A Simple Approach to Learning Unsupervised Multilingual Embeddings

no code implementations EMNLP 2020 Pratik Jawanpuria, Mayank Meghwanshi, Bamdev Mishra

Recent progress on unsupervised learning of cross-lingual embeddings in bilingual setting has given impetus to learning a shared embedding space for several languages without any supervision.

Bilingual Lexicon Induction Dependency Parsing +3

Geometry-aware Domain Adaptation for Unsupervised Alignment of Word Embeddings

no code implementations ACL 2020 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.

Bilingual Lexicon Induction Domain Adaptation +1

Riemannian optimization on the simplex of positive definite matrices

no code implementations25 Jun 2019 Bamdev Mishra, Hiroyuki Kasai, Pratik Jawanpuria

In this work, we generalize the probability simplex constraint to matrices, i. e., $\mathbf{X}_1 + \mathbf{X}_2 + \ldots + \mathbf{X}_K = \mathbf{I}$, where $\mathbf{X}_i \succeq 0$ is a symmetric positive semidefinite matrix of size $n\times n$ for all $i = \{1,\ldots, K \}$.

Riemannian optimization

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

no code implementations15 May 2019 Anil R. Yelundur, Vineet Chaoji, Bamdev Mishra

In this paper, our focus is on detecting such abusive entities (both sellers and reviewers) by applying tensor decomposition on the product reviews data.

Tensor Decomposition

Low-rank approximations of hyperbolic embeddings

no code implementations18 Mar 2019 Pratik Jawanpuria, Mayank Meghwanshi, Bamdev Mishra

While the hyperbolic manifold is well-studied in the literature, it has gained interest in the machine learning and natural language processing communities lately due to its usefulness in modeling continuous hierarchies.

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

no code implementations11 Feb 2019 Hiroyuki Kasai, Bamdev Mishra

Dictionary leaning (DL) and dimensionality reduction (DR) are powerful tools to analyze high-dimensional noisy signals.

Classification Dictionary Learning +4

Riemannian adaptive stochastic gradient algorithms on matrix manifolds

1 code implementation4 Feb 2019 Hiroyuki Kasai, Pratik Jawanpuria, Bamdev Mishra

We propose novel stochastic gradient algorithms for problems on Riemannian matrix manifolds by adapting the row and column subspaces of gradients.

Inexact trust-region algorithms on Riemannian manifolds

1 code implementation NeurIPS 2018 Hiroyuki Kasai, Bamdev Mishra

We consider an inexact variant of the popular Riemannian trust-region algorithm for structured big-data minimization problems.

McTorch, a manifold optimization library for deep learning

1 code implementation3 Oct 2018 Mayank Meghwanshi, Pratik Jawanpuria, Anoop Kunchukuttan, Hiroyuki Kasai, Bamdev Mishra

In this paper, we introduce McTorch, a manifold optimization library for deep learning that extends PyTorch.

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

2 code implementations TACL 2019 Pratik Jawanpuria, Arjun Balgovind, Anoop Kunchukuttan, Bamdev Mishra

Our approach decouples learning the transformation from the source language to the target language into (a) learning rotations for language-specific embeddings to align them to a common space, and (b) learning a similarity metric in the common space to model similarities between the embeddings.

Bilingual Lexicon Induction Multilingual Word Embeddings +4

Riemannian Stochastic Recursive Gradient Algorithm with Retraction and Vector Transport and Its Convergence Analysis

1 code implementation ICML 2018 Hiroyuki Kasai, Hiroyuki Sato, Bamdev Mishra

Stochastic variance reduction algorithms have recently become popular for minimizing the average of a large, but finite number of loss functions on a Riemannian manifold.

A Unified Framework for Structured Low-rank Matrix Learning

1 code implementation ICML 2018 Pratik Jawanpuria, Bamdev Mishra

We consider the problem of learning a low-rank matrix, constrained to lie in a linear subspace, and introduce a novel factorization for modeling such matrices.

Matrix Completion Multi-Task Learning +1

Low-rank geometric mean metric learning

1 code implementation14 Jun 2018 Mukul Bhutani, Pratik Jawanpuria, Hiroyuki Kasai, Bamdev Mishra

We propose a low-rank approach to learning a Mahalanobis metric from data.

Metric Learning

A Unified Framework for Domain Adaptation using Metric Learning on Manifolds

1 code implementation28 Apr 2018 Sridhar Mahadevan, Bamdev Mishra, Shalini Ghosh

We present a novel framework for domain adaptation, whereby both geometric and statistical differences between a labeled source domain and unlabeled target domain can be integrated by exploiting the curved Riemannian geometry of statistical manifolds.

Domain Adaptation Metric Learning +1

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

no code implementations18 Feb 2018 Madhav Nimishakavi, Bamdev Mishra, Manish Gupta, Partha Talukdar

Besides the tensors, in many real world scenarios, side information is also available in the form of matrices which also grow in size with time.

A dual framework for low-rank tensor completion

no code implementations NeurIPS 2018 Madhav Nimishakavi, Pratik Jawanpuria, Bamdev Mishra

One of the popular approaches for low-rank tensor completion is to use the latent trace norm regularization.

Riemannian optimization

A two-dimensional decomposition approach for matrix completion through gossip

no code implementations21 Nov 2017 Mukul Bhutani, Bamdev Mishra

The problem of matrix completion especially uses it to decompose a sparse matrix into two non sparse, low rank matrices which can then be used to predict unknown entries of the original matrix.

Matrix Completion

A Riemannian gossip approach to subspace learning on Grassmann manifold

no code implementations1 May 2017 Bamdev Mishra, Hiroyuki Kasai, Pratik Jawanpuria, Atul Saroop

Interesting applications in this setting include low-rank matrix completion and low-dimensional multivariate regression, among others.

Low-Rank Matrix Completion

Structured low-rank matrix learning: algorithms and applications

no code implementations24 Apr 2017 Pratik Jawanpuria, Bamdev Mishra

We consider the problem of learning a low-rank matrix, constrained to lie in a linear subspace, and introduce a novel factorization for modeling such matrices.

Matrix Completion Multi-Task Learning

Riemannian stochastic variance reduced gradient algorithm with retraction and vector transport

1 code implementation18 Feb 2017 Hiroyuki Sato, Hiroyuki Kasai, Bamdev Mishra

In recent years, stochastic variance reduction algorithms have attracted considerable attention for minimizing the average of a large but finite number of loss functions.

Low-Rank Matrix Completion

Low-rank tensor completion: a Riemannian manifold preconditioning approach

no code implementations26 May 2016 Hiroyuki Kasai, Bamdev Mishra

We propose a novel Riemannian manifold preconditioning approach for the tensor completion problem with rank constraint.

Riemannian optimization

Riemannian stochastic variance reduced gradient on Grassmann manifold

1 code implementation24 May 2016 Hiroyuki Kasai, Hiroyuki Sato, Bamdev Mishra

In this paper, we propose a novel Riemannian extension of the Euclidean stochastic variance reduced gradient algorithm (R-SVRG) to a compact manifold search space.

Low-Rank Matrix Completion Translation

A Riemannian gossip approach to decentralized matrix completion

no code implementations23 May 2016 Bamdev Mishra, Hiroyuki Kasai, Atul Saroop

In this paper, we propose novel gossip algorithms for the low-rank decentralized matrix completion problem.

Matrix Completion

Scaled stochastic gradient descent for low-rank matrix completion

no code implementations16 Mar 2016 Bamdev Mishra, Rodolphe Sepulchre

The paper looks at a scaled variant of the stochastic gradient descent algorithm for the matrix completion problem.

Low-Rank Matrix Completion

Symmetry-invariant optimization in deep networks

no code implementations5 Nov 2015 Vijay Badrinarayanan, Bamdev Mishra, Roberto Cipolla

Recent works have highlighted scale invariance or symmetry that is present in the weight space of a typical deep network and the adverse effect that it has on the Euclidean gradient based stochastic gradient descent optimization.

Semantic Segmentation

Understanding symmetries in deep networks

no code implementations3 Nov 2015 Vijay Badrinarayanan, Bamdev Mishra, Roberto Cipolla

Consequently, training the network boils down to using stochastic gradient descent updates on the unit-norm manifold.

Riemannian preconditioning for tensor completion

no code implementations6 Jun 2015 Hiroyuki Kasai, Bamdev Mishra

We propose a novel Riemannian preconditioning approach for the tensor completion problem with rank constraint.

Riemannian optimization

Heterogeneous Tensor Decomposition for Clustering via Manifold Optimization

no code implementations7 Apr 2015 Yanfeng Sun, Junbin Gao, Xia Hong, Bamdev Mishra, Bao-Cai Yin

In contrast to existing techniques, we propose a new clustering algorithm that alternates between different modes of the proposed heterogeneous tensor model.

Tensor Decomposition

Sparse plus low-rank autoregressive identification in neuroimaging time series

no code implementations30 Mar 2015 Raphaël Liégeois, Bamdev Mishra, Mattia Zorzi, Rodolphe Sepulchre

This paper considers the problem of identifying multivariate autoregressive (AR) sparse plus low-rank graphical models.

Time Series

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