Search Results for author: Shuchin Aeron

Found 54 papers, 17 papers with code

Accuracy versus time frontiers of semi-supervised and self-supervised learning on medical images

1 code implementation18 Jul 2023 Zhe Huang, Ruijie Jiang, Shuchin Aeron, Michael C. Hughes

This study contributes a carefully-designed benchmark to help answer a practitioner's key question: given a small labeled dataset and a limited budget of hours to spend on training, what gains from additional unlabeled images are possible and which methods best achieve them?

Self-Supervised Learning

A principled approach to model validation in domain generalization

1 code implementation2 Apr 2023 Boyang Lyu, Thuan Nguyen, Matthias Scheutz, Prakash Ishwar, Shuchin Aeron

Domain generalization aims to learn a model with good generalization ability, that is, the learned model should not only perform well on several seen domains but also on unseen domains with different data distributions.

Classification Domain Generalization +1

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

no code implementations15 Feb 2023 Matthew Werenski, Shoaib Bin Masud, James M. Murphy, Shuchin Aeron

This paper considers the use of recently proposed optimal transport-based multivariate test statistics, namely rank energy and its variant the soft rank energy derived from entropically regularized optimal transport, for the unsupervised nonparametric change point detection (CPD) problem.

Change Point Detection

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

no code implementations14 Nov 2022 Ahmed Abbasi, Abiy Tasissa, Shuchin Aeron

The unlabeled sensing problem is to recover an unknown signal from permuted linear measurements.

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

1 code implementation26 Oct 2022 Thuan Nguyen, Boyang Lyu, Prakash Ishwar, Matthias Scheutz, Shuchin Aeron

To deal with challenging settings in DG where both data and label of the unseen domain are not available at training time, the most common approach is to design the classifiers based on the domain-invariant representation features, i. e., the latent representations that are unchanged and transferable between domains.

Domain Generalization Transfer Learning

Geometric Sparse Coding in Wasserstein Space

no code implementations21 Oct 2022 Marshall Mueller, Shuchin Aeron, James M. Murphy, Abiy Tasissa

We show this approach leads to sparse representations in Wasserstein space and addresses the problem of non-uniqueness of barycentric representation.

Dictionary Learning

Nonparametric and Regularized Dynamical Wasserstein Barycenters for Sequential Observations

no code implementations4 Oct 2022 Kevin C. Cheng, Shuchin Aeron, Michael C. Hughes, Eric L. Miller

We consider probabilistic models for sequential observations which exhibit gradual transitions among a finite number of states.

Time Series Time Series Analysis

Supervised Contrastive Learning with Hard Negative Samples

1 code implementation31 Aug 2022 Ruijie Jiang, Thuan Nguyen, Prakash Ishwar, Shuchin Aeron

In this paper, motivated by the effectiveness of hard-negative sampling strategies in H-UCL and the usefulness of label information in SCL, we propose a contrastive learning framework called hard-negative supervised contrastive learning (H-SCL).

Contrastive Learning Self-Supervised Learning

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

1 code implementation1 Aug 2022 Thuan Nguyen, Boyang Lyu, Prakash Ishwar, Matthias Scheutz, Shuchin Aeron

Particularly, our framework proposes to jointly minimize both the covariate-shift as well as the concept-shift between the seen domains for a better performance on the unseen domain.

Concept Alignment Domain Generalization

Easy Variational Inference for Categorical Models via an Independent Binary Approximation

1 code implementation31 May 2022 Michael T. Wojnowicz, Shuchin Aeron, Eric L. Miller, Michael C. Hughes

This approximation makes inference straightforward and fast; using well-known auxiliary variables for probit or logistic regression, the product of binary models admits conjugate closed-form variational inference that is embarrassingly parallel across categories and invariant to category ordering.

Variational Inference

Measure Estimation in the Barycentric Coding Model

1 code implementation28 Jan 2022 Matthew Werenski, Ruijie Jiang, Abiy Tasissa, Shuchin Aeron, James M. Murphy

Our first main result leverages the Riemannian geometry of Wasserstein-2 space to provide a procedure for recovering the barycentric coordinates as the solution to a quadratic optimization problem assuming access to the true reference measures.

Conditional entropy minimization principle for learning domain invariant representation features

2 code implementations25 Jan 2022 Thuan Nguyen, Boyang Lyu, Prakash Ishwar, Matthias Scheutz, Shuchin Aeron

Invariance-principle-based methods such as Invariant Risk Minimization (IRM), have recently emerged as promising approaches for Domain Generalization (DG).

Domain Generalization

Hard Negative Sampling via Regularized Optimal Transport for Contrastive Representation Learning

1 code implementation4 Nov 2021 Ruijie Jiang, Prakash Ishwar, Shuchin Aeron

We analyze a novel min-max framework that seeks a representation which minimizes the maximum (worst-case) generalized contrastive learning loss over all couplings (joint distributions between positive and negative samples subject to marginal constraints) and prove that the resulting min-max optimum representation will be degenerate.

Contrastive Learning Representation Learning

Interpretable contrastive word mover's embedding

1 code implementation1 Nov 2021 Ruijie Jiang, Julia Gouvea, Eric Miller, David Hammer, Shuchin Aeron

This paper shows that a popular approach to the supervised embedding of documents for classification, namely, contrastive Word Mover's Embedding, can be significantly enhanced by adding interpretability.

Multivariate rank via entropic optimal transport: sample efficiency and generative modeling

no code implementations29 Oct 2021 Shoaib Bin Masud, Matthew Werenski, James M. Murphy, Shuchin Aeron

We leverage this result to demonstrate fast convergence of sample sRE and sRMMD to their population version making them useful for high-dimensional GoF testing.

feature selection Image Generation +1

r-local sensing: Improved algorithm and applications

2 code implementations26 Oct 2021 Ahmed Ali Abbasi, Abiy Tasissa, Shuchin Aeron

The unlabeled sensing problem is to solve a noisy linear system of equations under unknown permutation of the measurements.

Dynamical Wasserstein Barycenters for Time-series Modeling

1 code implementation NeurIPS 2021 Kevin C. Cheng, Shuchin Aeron, Michael C. Hughes, Eric L. Miller

We propose a dynamical Wasserstein barycentric (DWB) model that estimates the system state over time as well as the data-generating distributions of pure states in an unsupervised manner.

Time Series Time Series Analysis

Barycentric-alignment and reconstruction loss minimization for domain generalization

1 code implementation4 Sep 2021 Boyang Lyu, Thuan Nguyen, Prakash Ishwar, Matthias Scheutz, Shuchin Aeron

To bridge this gap between theory and practice, we introduce a new upper bound that is free of terms having such dual dependence, resulting in a fully optimizable risk upper bound for the unseen domain.

Domain Generalization Representation Learning

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

1 code implementation16 Mar 2021 Shoaib Bin Masud, Boyang Lyu, Shuchin Aeron

In this paper, we extend the recently proposed multivariate rank energy distance, based on the theory of optimal transport, for statistical testing of distributional similarity, to soft rank energy distance.

Change Point Detection Time Series +1

Multiview Sensing With Unknown Permutations: An Optimal Transport Approach

no code implementations12 Mar 2021 Yanting Ma, Petros T. Boufounos, Hassan Mansour, Shuchin Aeron

In several applications, including imaging of deformable objects while in motion, simultaneous localization and mapping, and unlabeled sensing, we encounter the problem of recovering a signal that is measured subject to unknown permutations.

Simultaneous Localization and Mapping

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

no code implementations26 Nov 2020 Ruijie Jiang, Julia Gouvea, David Hammer, Eric Miller, Shuchin Aeron

This work is a step towards building a statistical machine learning (ML) method for achieving an automated support for qualitative analyses of students' writing, here specifically in score laboratory reports in introductory biology for sophistication of argumentation and reasoning.

BIG-bench Machine Learning Contrastive Learning +4

Robust Machine Learning via Privacy/Rate-Distortion Theory

no code implementations22 Jul 2020 Ye Wang, Shuchin Aeron, Adnan Siraj Rakin, Toshiaki Koike-Akino, Pierre Moulin

Robust machine learning formulations have emerged to address the prevalent vulnerability of deep neural networks to adversarial examples.

BIG-bench Machine Learning

Representation Learning via Adversarially-Contrastive Optimal Transport

no code implementations ICML 2020 Anoop Cherian, Shuchin Aeron

To maximize extraction of such informative cues from the data, we set the problem within the context of contrastive representation learning and to that end propose a novel objective via optimal transport.

Action Recognition Contrastive Learning +3

Domain Adaptation for Robust Workload Level Alignment Between Sessions and Subjects using fNIRS

no code implementations2 Jul 2020 Boyang Lyu, Thao Pham, Giles Blaney, Zachary Haga, Angelo Sassaroli, Sergio Fantini, Shuchin Aeron

Results: In a sample of six subjects, G-W resulted in an alignment accuracy of 68 $\pm$ 4 % (weighted mean $\pm$ standard error) for session-by-session alignment, FG-W resulted in an alignment accuracy of 55 $\pm$ 2 % for subject-by-subject alignment.

Domain Adaptation

On Matched Filtering for Statistical Change Point Detection

no code implementations9 Jun 2020 Kevin C. Cheng, Eric L. Miller, Michael C. Hughes, Shuchin Aeron

Non-parametric and distribution-free two-sample tests have been the foundation of many change point detection algorithms.

Activity Recognition Change Point Detection

R-local unlabeled sensing: A novel graph matching approach for multiview unlabeled sensing under local permutations

1 code implementation14 Nov 2019 Ahmed Abbasi, Abiy Tasissa, Shuchin Aeron

Unlabeled sensing is a linear inverse problem where the measurements are scrambled under an unknown permutation leading to loss of correspondence between the measurements and the rows of the sensing matrix.

Graph Matching

Optimal Transport Based Change Point Detection and Time Series Segment Clustering

no code implementations4 Nov 2019 Kevin C. Cheng, Shuchin Aeron, Michael C. Hughes, Erika Hussey, Eric L. Miller

Two common problems in time series analysis are the decomposition of the data stream into disjoint segments that are each in some sense "homogeneous" - a problem known as Change Point Detection (CPD) - and the grouping of similar nonadjacent segments, a problem that we call Time Series Segment Clustering (TSSC).

Change Point Detection Clustering +2

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

no code implementations2 Aug 2019 Yanting Ma, Shuchin Aeron, Hassan Mansour

In this article, we study the convergence of Mirror Descent (MD) and Optimistic Mirror Descent (OMD) for saddle point problems satisfying the notion of coherence as proposed in Mertikopoulos et al. We prove convergence of OMD with exact gradients for coherent saddle point problems, and show that monotone convergence only occurs after some sufficiently large number of iterations.

Multi-View Graph Embedding Using Randomized Shortest Paths

no code implementations20 Aug 2018 Anuththari Gamage, Brian Rappaport, Shuchin Aeron, Xiaozhe Hu

This data is well represented by multi-view graphs, which consist of several distinct sets of edges over the same nodes.

Clustering Graph Embedding

Principal Component Analysis with Tensor Train Subspace

no code implementations13 Mar 2018 Wenqi Wang, Vaneet Aggarwal, Shuchin Aeron

Tensor train is a hierarchical tensor network structure that helps alleviate the curse of dimensionality by parameterizing large-scale multidimensional data via a set of network of low-rank tensors.


no code implementations ICLR 2018 Eric Bailey, Charles Meyer, Shuchin Aeron

We present two new word embedding techniques based on tensor factorization and show that they outperform common methods on several semantic NLP tasks when given the same data.

Outlier Detection

Tensor Train Neighborhood Preserving Embedding

no code implementations3 Dec 2017 Wenqi Wang, Vaneet Aggarwal, Shuchin Aeron

In this paper, we propose a Tensor Train Neighborhood Preserving Embedding (TTNPE) to embed multi-dimensional tensor data into low dimensional tensor subspace.

Classification Dimensionality Reduction +1

Faster Clustering via Non-Backtracking Random Walks

no code implementations26 Aug 2017 Brian Rappaport, Anuththari Gamage, Shuchin Aeron

VEC employs a novel application of the state-of-the-art word2vec model to embed a graph in Euclidean space via random walks on the nodes of the graph.

Clustering Graph Clustering

Efficient Low Rank Tensor Ring Completion

no code implementations ICCV 2017 Wenqi Wang, Vaneet Aggarwal, Shuchin Aeron

Using the matrix product state (MPS) representation of the recently proposed tensor ring decompositions, in this paper we propose a tensor completion algorithm, which is an alternating minimization algorithm that alternates over the factors in the MPS representation.

Matrix Completion

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

no code implementations18 Jun 2017 Mohammadhossein Chaghazardi, Shuchin Aeron

Our main tool is the use of tensor subspaces, i. e. subspaces with a Kronecker structure, for embedding the data into lower dimensions.

General Classification

Word Embeddings via Tensor Factorization

1 code implementation10 Apr 2017 Eric Bailey, Shuchin Aeron

We show that embeddings based on tensor factorization can be used to discern the various meanings of polysemous words without being explicitly trained to do so, and motivate the intuition behind why this works in a way that doesn't with existing methods.

Outlier Detection Word Embeddings

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

no code implementations15 Oct 2016 Wenqi Wang, Vaneet Aggarwal, Shuchin Aeron

Similar to the Union of Subspaces (UOS) model where each data from each subspace is generated from a (unknown) basis, in the UOPC model each data from each cone is assumed to be generated from a finite number of (unknown) \emph{extreme rays}. To cluster data under this model, we consider several algorithms - (a) Sparse Subspace Clustering by Non-negative constraints Lasso (NCL), (b) Least squares approximation (LSA), and (c) K-nearest neighbor (KNN) algorithm to arrive at affinity between data points.


Low-tubal-rank Tensor Completion using Alternating Minimization

no code implementations5 Oct 2016 Xiao-Yang Liu, Shuchin Aeron, Vaneet Aggarwal, Xiaodong Wang

The low-tubal-rank tensor model has been recently proposed for real-world multidimensional data.

Low-Rank Matrix Completion

Algorithms for item categorization based on ordinal ranking data

no code implementations29 Sep 2016 Josh Girson, Shuchin Aeron

In this context we modify an existing algorithm - namely the label propagation algorithm to a variant that uses the distance between the nodes for weighting the label propagation - to identify the categories.

Community Detection Stochastic Block Model +1

Tensor Completion by Alternating Minimization under the Tensor Train (TT) Model

no code implementations19 Sep 2016 Wenqi Wang, Vaneet Aggarwal, Shuchin Aeron

Using the matrix product state (MPS) representation of tensor train decompositions, in this paper we propose a tensor completion algorithm which alternates over the matrices (tensors) in the MPS representation.

Matrix Completion

On Deterministic Conditions for Subspace Clustering under Missing Data

no code implementations11 Jul 2016 Wenqi Wang, Shuchin Aeron, Vaneet Aggarwal

In this paper we present deterministic conditions for success of sparse subspace clustering (SSC) under missing data, when data is assumed to come from a Union of Subspaces (UoS) model.


On deterministic conditions for subspace clustering under missing data

no code implementations15 Apr 2016 Wenqi Wang, Shuchin Aeron, Vaneet Aggarwal

We provide extensive set of simulation results for clustering as well as completion of data under missing entries, under the UoS model.


Multilinear Subspace Clustering

no code implementations21 Dec 2015 Eric Kernfeld, Nathan Majumder, Shuchin Aeron, Misha Kilmer

In this paper we present a new model and an algorithm for unsupervised clustering of 2-D data such as images.


Group-Invariant Subspace Clustering

no code implementations15 Oct 2015 Shuchin Aeron, Eric Kernfeld

In this paper we consider the problem of group invariant subspace clustering where the data is assumed to come from a union of group-invariant subspaces of a vector space, i. e. subspaces which are invariant with respect to action of a given group.


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

no code implementations14 Aug 2015 Vaneet Aggarwal, Shuchin Aeron

In this short note we extend some of the recent results on matrix completion under the assumption that the columns of the matrix can be grouped (clustered) into subspaces (not necessarily disjoint or independent).

Clustering Matrix Completion

Adaptive Sampling of RF Fingerprints for Fine-grained Indoor Localization

no code implementations10 Aug 2015 Xiao-Yang Liu, Shuchin Aeron, Vaneet Aggarwal, Xiaodong Wang, Min-You Wu

In contrast to several existing work that rely on random sampling, this paper shows that adaptivity in sampling can lead to significant improvements in localization accuracy.

Indoor Localization

An algorithm for online tensor prediction

no code implementations28 Jul 2015 John Pothier, Josh Girson, Shuchin Aeron

Then following a similar construction as in [3], we exploit this algorithm to propose an online algorithm for learning and prediction of tensors with provable regret guarantees.

Exact tensor completion using t-SVD

no code implementations16 Feb 2015 Zemin Zhang, Shuchin Aeron

Using this factorization one can derive notion of tensor rank, referred to as the tensor tubal rank, which has optimality properties similar to that of matrix rank derived from SVD.

Matrix Completion

Clustering multi-way data: a novel algebraic approach

no code implementations22 Dec 2014 Eric Kernfeld, Shuchin Aeron, Misha Kilmer

In this paper, we develop a method for unsupervised clustering of two-way (matrix) data by combining two recent innovations from different fields: the Sparse Subspace Clustering (SSC) algorithm [10], which groups points coming from a union of subspaces into their respective subspaces, and the t-product [18], which was introduced to provide a matrix-like multiplication for third order tensors.

Clustering Image Clustering

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

2 code implementations CVPR 2014 Zemin Zhang, Gregory Ely, Shuchin Aeron, Ning Hao, Misha Kilmer

Based on t-SVD, the notion of multilinear rank and a related tensor nuclear norm was proposed in [11] to characterize informational and structural complexity of multilinear data.

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

no code implementations24 Mar 2014 Jason Gejie Liu, Shuchin Aeron

Nonnegative matrix factorization (NMF) has been shown to be identifiable under the separability assumption, under which all the columns(or rows) of the input data matrix belong to the convex cone generated by only a few of these columns(or rows) [1].

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

no code implementations8 Jan 2014 Jason Gejie Liu, Shuchin Aeron

A robust algorithm for non-negative matrix factorization (NMF) is presented in this paper with the purpose of dealing with large-scale data, where the separability assumption is satisfied.

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