b'Shuchin Aeron'

research

∙ 04/02/2023

A principled approach to model validation in domain generalization

Domain generalization aims to learn a model with good generalization abi...

0 Boyang Lyu, et al. ∙

research

∙ 02/15/2023

On Rank Energy Statistics via Optimal Transport: Continuity, Convergence, and Change Point Detection

This paper considers the use of recently proposed optimal transport-base...

0 Matthew Werenski, et al. ∙

research

∙ 11/14/2022

Alternating minimization algorithm with initialization analysis for r-local and k-sparse unlabeled sensing

The unlabeled sensing problem is to recover an unknown signal from permu...

0 Ahmed Abbasi, et al. ∙

research

∙ 10/26/2022

Trade-off between reconstruction loss and feature alignment for domain generalization

Domain generalization (DG) is a branch of transfer learning that aims to...

0 Thuan Nguyen, et al. ∙

research

∙ 10/21/2022

Geometric Sparse Coding in Wasserstein Space

Wasserstein dictionary learning is an unsupervised approach to learning ...

0 Marshall Mueller, et al. ∙

research

∙ 10/04/2022

Non-Parametric and Regularized Dynamical Wasserstein Barycenters for Time-Series Analysis

We consider probabilistic time-series models for systems that gradually ...

0 Kevin C. Cheng, et al. ∙

research

∙ 08/31/2022

Supervised Contrastive Learning with Hard Negative Samples

Unsupervised contrastive learning (UCL) is a self-supervised learning te...

0 Ruijie Jiang, et al. ∙

research

∙ 08/01/2022

Joint covariate-alignment and concept-alignment: a framework for domain generalization

In this paper, we propose a novel domain generalization (DG) framework b...

0 Thuan Nguyen, et al. ∙

research

∙ 05/31/2022

Easy Variational Inference for Categorical Models via an Independent Binary Approximation

We pursue tractable Bayesian analysis of generalized linear models (GLMs...

0 Michael T. Wojnowicz, et al. ∙

research

∙ 01/28/2022

Measure Estimation in the Barycentric Coding Model

This paper considers the problem of measure estimation under the barycen...

0 Matthew Werenski, et al. ∙

research

∙ 01/25/2022

Conditional entropy minimization principle for learning domain invariant representation features

Invariance principle-based methods, for example, Invariant Risk Minimiza...

0 Thuan Nguyen, et al. ∙

research

∙ 11/04/2021

Hard Negative Sampling via Regularized Optimal Transport for Contrastive Representation Learning

We study the problem of designing hard negative sampling distributions f...

0 Ruijie Jiang, et al. ∙

research

∙ 11/01/2021

Interpretable contrastive word mover's embedding

This paper shows that a popular approach to the supervised embedding of ...

0 Ruijie Jiang, et al. ∙

research

∙ 10/29/2021

Robust and efficient change point detection using novel multivariate rank-energy GoF test

In this paper, we use and further develop upon a recently proposed multi...

0 Shoaib Bin Masud, et al. ∙

research

∙ 10/29/2021

Learning generative models for valid knockoffs using novel multivariate-rank based statistics

We consider the problem of generating valid knockoffs for knockoff filte...

0 Shoaib Bin Masud, et al. ∙

research

∙ 10/26/2021

r-local sensing: Improved algorithm and applications

The unlabeled sensing problem is to solve a noisy linear system of equat...

0 Ahmed Ali Abbasi, et al. ∙

research

∙ 10/13/2021

Dynamical Wasserstein Barycenters for Time-series Modeling

Many time series can be modeled as a sequence of segments representing h...

0 Kevin C. Cheng, et al. ∙

research

∙ 09/04/2021

Barycenteric distribution alignment and manifold-restricted invertibility for domain generalization

For the Domain Generalization (DG) problem where the hypotheses are comp...

0 Boyang Lyu, et al. ∙

research

∙ 03/16/2021

Soft and subspace robust multivariate rank tests based on entropy regularized optimal transport

In this paper, we extend the recently proposed multivariate rank energy ...

0 Shoaib Bin Masud, et al. ∙

research

∙ 03/12/2021

Multiview Sensing With Unknown Permutations: An Optimal Transport Approach

In several applications, including imaging of deformable objects while i...

0 Yanting Ma, et al. ∙

research

∙ 11/26/2020

Automatic coding of students' writing via Contrastive Representation Learning in the Wasserstein space

Qualitative analysis of verbal data is of central importance in the lear...

0 Ruijie Jiang, et al. ∙

research

∙ 07/22/2020

Robust Machine Learning via Privacy/Rate-Distortion Theory

Robust machine learning formulations have emerged to address the prevale...

1 Ye Wang, et al. ∙

research

∙ 07/11/2020

Representation Learning via Adversarially-Contrastive Optimal Transport

In this paper, we study the problem of learning compact (low-dimensional...

0 Anoop Cherian, et al. ∙

research

∙ 06/09/2020

On Matched Filtering for Statistical Change Point Detection

Non-parametric and distribution-free two-sample tests have been the foun...

0 Kevin C. Cheng, et al. ∙

research

∙ 11/04/2019

Optimal Transport Based Change Point Detection and Time Series Segment Clustering

Two common problems in time series analysis are the decomposition of the...

0 Kevin C. Cheng, et al. ∙

research

∙ 08/02/2019

On the modes of convergence of Stochastic Optimistic Mirror Descent (OMD) for saddle point problems

In this article, we study the convergence of Mirror Descent (MD) and Opt...

0 Yanting Ma, et al. ∙

research

∙ 08/20/2018

Multi-View Graph Embedding Using Randomized Shortest Paths

Real-world data sets often provide multiple types of information about t...

0 Anuththari Gamage, et al. ∙

research

∙ 03/13/2018

Principal Component Analysis with Tensor Train Subspace

Tensor train is a hierarchical tensor network structure that helps allev...

0 Wenqi Wang, et al. ∙

research

∙ 12/03/2017

Tensor Train Neighborhood Preserving Embedding

In this paper, we propose a Tensor Train Neighborhood Preserving Embeddi...

0 Wenqi Wang, et al. ∙

research

∙ 08/26/2017

Faster Clustering via Non-Backtracking Random Walks

This paper presents VEC-NBT, a variation on the unsupervised graph clust...

0 Brian Rappaport, et al. ∙

research

∙ 06/18/2017

Sample, computation vs storage tradeoffs for classification using tensor subspace models

In this paper, we exhibit the tradeoffs between the (training) sample, c...

0 Mohammadhossein Chaghazardi, et al. ∙

research

∙ 04/10/2017

Word Embeddings via Tensor Factorization

Most popular word embedding techniques involve implicit or explicit fact...

0 Eric Bailey, et al. ∙

research

∙ 10/15/2016

Unsupervised clustering under the Union of Polyhedral Cones (UOPC) model

In this paper, we consider clustering data that is assumed to come from ...

0 Wenqi Wang, et al. ∙

research

∙ 07/11/2016

On Deterministic Conditions for Subspace Clustering under Missing Data

In this paper we present deterministic conditions for success of sparse ...

0 Wenqi Wang, et al. ∙

research

∙ 12/31/2015

Denoising and Completion of 3D Data via Multidimensional Dictionary Learning

In this paper a new dictionary learning algorithm for multidimensional d...

0 Zemin Zhang, et al. ∙

research

∙ 12/21/2015

Multilinear Subspace Clustering

In this paper we present a new model and an algorithm for unsupervised c...

0 Eric Kernfeld, et al. ∙

research

∙ 10/15/2015

Group-Invariant Subspace Clustering

In this paper we consider the problem of group invariant subspace cluste...

0 Shuchin Aeron, et al. ∙

research

∙ 08/14/2015

Information-theoretic Bounds on Matrix Completion under Union of Subspaces Model

In this short note we extend some of the recent results on matrix comple...

0 Vaneet Aggarwal, et al. ∙

research

∙ 08/10/2015

Adaptive Sampling of RF Fingerprints for Fine-grained Indoor Localization

Indoor localization is a supporting technology for a broadening range of...

0 Xiao-Yang Liu, et al. ∙

research

∙ 02/16/2015

Exact tensor completion using t-SVD

In this paper we focus on the problem of completion of multidimensional ...

0 Zemin Zhang, et al. ∙

research

∙ 12/22/2014

Clustering multi-way data: a novel algebraic approach

In this paper, we develop a method for unsupervised clustering of two-wa...

0 Eric Kernfeld, et al. ∙

research

∙ 07/07/2014

Novel methods for multilinear data completion and de-noising based on tensor-SVD

In this paper we propose novel methods for completion (from limited samp...

0 Zemin Zhang, et al. ∙

research

∙ 03/24/2014

First Order Methods for Robust Non-negative Matrix Factorization for Large Scale Noisy Data

Nonnegative matrix factorization (NMF) has been shown to be identifiable...

0 Jason Gejie Liu, et al. ∙

research

∙ 01/08/2014

Robust Large Scale Non-negative Matrix Factorization using Proximal Point Algorithm

A robust algorithm for non-negative matrix factorization (NMF) is presen...

0 Jason Gejie Liu, et al. ∙

research

∙ 07/02/2013

Novel Factorization Strategies for Higher Order Tensors: Implications for Compression and Recovery of Multi-linear Data

In this paper we propose novel methods for compression and recovery of m...

0 Zemin Zhang, et al. ∙

Shuchin Aeron

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