
SPOT: A framework for selection of prototypes using optimal transport
In this work, we develop an optimal transport (OT) based framework to se...
read it

Manifold optimization for optimal transport
Optimal transport (OT) has recently found widespread interest in machine...
read it

Efficient robust optimal transport: formulations and algorithms
The problem of robust optimal transport (OT) aims at recovering the best...
read it

Learning Geometric Word MetaEmbeddings
We propose a geometric framework for learning metaembeddings of words f...
read it

A Simple Approach to Learning Unsupervised Multilingual Embeddings
Recent progress on unsupervised learning of crosslingual embeddings in ...
read it

Riemannian optimization on the simplex of positive definite matrices
We discuss optimizationrelated ingredients for the Riemannian manifold ...
read it

Detection of Review Abuse via SemiSupervised Binary MultiTarget Tensor Decomposition
Product reviews and ratings on ecommerce websites provide customers wit...
read it

Lowrank approximations of hyperbolic embeddings
The hyperbolic manifold is a smooth manifold of negative constant curvat...
read it

Riemannian joint dimensionality reduction and dictionary learning on symmetric positive definite manifold
Dictionary leaning (DL) and dimensionality reduction (DR) are powerful t...
read it

Adaptive stochastic gradient algorithms on Riemannian manifolds
Adaptive stochastic gradient algorithms in the Euclidean space have attr...
read it

McTorch, a manifold optimization library for deep learning
In this paper, we introduce McTorch, a manifold optimization library for...
read it

Learning Multilingual Word Embeddings in Latent Metric Space: A Geometric Approach
We propose a novel geometric approach for learning bilingual mappings gi...
read it

Learning Multilingual Word Embeddings in a Latent Metric Space: A Geometric Approach
We propose a novel geometric approach for learning bilingual mappings gi...
read it

Lowrank geometric mean metric learning
We propose a lowrank approach to learning a Mahalanobis metric from dat...
read it

A Unified Framework for Domain Adaptation using Metric Learning on Manifolds
We present a novel framework for domain adaptation, whereby both geometr...
read it

Bayesian SemiSupervised Tensor Decomposition using Natural Gradients for Anomaly Detection
Anomaly Detection has several important applications. In this paper, our...
read it

Inductive Framework for MultiAspect Streaming Tensor Completion with Side Information
Lowrank tensor completion is a wellstudied problem and has application...
read it

A Dual Framework for Lowrank Tensor Completion
We propose a novel formulation of the lowrank tensor completion problem...
read it

A Saddle Point Approach to Structured Lowrank Matrix Learning
We propose a novel optimization approach for learning a lowrank matrix ...
read it

Riemannian stochastic quasiNewton algorithm with variance reduction and its convergence analysis
Stochastic variance reduction algorithms have recently become popular fo...
read it

Riemannian stochastic variance reduced gradient
Stochastic variance reduction algorithms have recently become popular fo...
read it

Lowrank tensor completion: a Riemannian manifold preconditioning approach
We propose a novel Riemannian manifold preconditioning approach for the ...
read it

Riemannian stochastic variance reduced gradient on Grassmann manifold
Stochastic variance reduction algorithms have recently become popular fo...
read it

Symmetryinvariant optimization in deep networks
Recent works have highlighted scale invariance or symmetry that is prese...
read it

Understanding symmetries in deep networks
Recent works have highlighted scale invariance or symmetry present in th...
read it

Heterogeneous Tensor Decomposition for Clustering via Manifold Optimization
Tensors or multiarray data are generalizations of matrices. Tensor clust...
read it

Manopt, a Matlab toolbox for optimization on manifolds
Optimization on manifolds is a rapidly developing branch of nonlinear op...
read it