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04/20/2023
Light-weight Deep Extreme Multilabel Classification
Extreme multi-label (XML) classification refers to the task of supervise...
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11/30/2022
Generalised Spherical Text Embedding
This paper aims to provide an unsupervised modelling approach that allow...
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10/10/2022
Rieoptax: Riemannian Optimization in JAX
We present Rieoptax, an open source Python library for Riemannian optimi...
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10/04/2022
ProtoBandit: Efficient Prototype Selection via Multi-Armed Bandits
In this work, we propose a multi-armed bandit based framework for identi...
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03/18/2021
SPOT: A framework for selection of prototypes using optimal transport
In this work, we develop an optimal transport (OT) based framework to se...
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03/01/2021
Manifold optimization for optimal transport
Optimal transport (OT) has recently found widespread interest in machine...
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10/22/2020
Efficient robust optimal transport: formulations and algorithms
The problem of robust optimal transport (OT) aims at recovering the best...
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04/20/2020
Learning Geometric Word Meta-Embeddings
We propose a geometric framework for learning meta-embeddings of words f...
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04/10/2020
A Simple Approach to Learning Unsupervised Multilingual Embeddings
Recent progress on unsupervised learning of cross-lingual embeddings in ...
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06/25/2019
Riemannian optimization on the simplex of positive definite matrices
We discuss optimization-related ingredients for the Riemannian manifold ...
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05/15/2019
Detection of Review Abuse via Semi-Supervised Binary Multi-Target Tensor Decomposition
Product reviews and ratings on e-commerce websites provide customers wit...
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03/18/2019
Low-rank approximations of hyperbolic embeddings
The hyperbolic manifold is a smooth manifold of negative constant curvat...
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02/11/2019
Riemannian joint dimensionality reduction and dictionary learning on symmetric positive definite manifold
Dictionary leaning (DL) and dimensionality reduction (DR) are powerful t...
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02/04/2019
Adaptive stochastic gradient algorithms on Riemannian manifolds
Adaptive stochastic gradient algorithms in the Euclidean space have attr...
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10/03/2018
McTorch, a manifold optimization library for deep learning
In this paper, we introduce McTorch, a manifold optimization library for...
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08/27/2018
Learning Multilingual Word Embeddings in Latent Metric Space: A Geometric Approach
We propose a novel geometric approach for learning bilingual mappings gi...
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08/27/2018
Learning Multilingual Word Embeddings in a Latent Metric Space: A Geometric Approach
We propose a novel geometric approach for learning bilingual mappings gi...
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06/14/2018
Low-rank geometric mean metric learning
We propose a low-rank approach to learning a Mahalanobis metric from dat...
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04/28/2018
A Unified Framework for Domain Adaptation using Metric Learning on Manifolds
We present a novel framework for domain adaptation, whereby both geometr...
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04/11/2018
Bayesian Semi-Supervised Tensor Decomposition using Natural Gradients for Anomaly Detection
Anomaly Detection has several important applications. In this paper, our...
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02/18/2018
Inductive Framework for Multi-Aspect Streaming Tensor Completion with Side Information
Low-rank tensor completion is a well-studied problem and has application...
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12/04/2017
A Dual Framework for Low-rank Tensor Completion
We propose a novel formulation of the low-rank tensor completion problem...
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04/24/2017
A Saddle Point Approach to Structured Low-rank Matrix Learning
We propose a novel optimization approach for learning a low-rank matrix ...
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03/15/2017
Riemannian stochastic quasi-Newton algorithm with variance reduction and its convergence analysis
Stochastic variance reduction algorithms have recently become popular fo...
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02/18/2017
Riemannian stochastic variance reduced gradient
Stochastic variance reduction algorithms have recently become popular fo...
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05/26/2016
Low-rank tensor completion: a Riemannian manifold preconditioning approach
We propose a novel Riemannian manifold preconditioning approach for the ...
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05/24/2016
Riemannian stochastic variance reduced gradient on Grassmann manifold
Stochastic variance reduction algorithms have recently become popular fo...
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11/05/2015
Symmetry-invariant optimization in deep networks
Recent works have highlighted scale invariance or symmetry that is prese...
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11/03/2015
Understanding symmetries in deep networks
Recent works have highlighted scale invariance or symmetry present in th...
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04/07/2015
Heterogeneous Tensor Decomposition for Clustering via Manifold Optimization
Tensors or multiarray data are generalizations of matrices. Tensor clust...
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08/23/2013