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20242026
most citedDiscriminative reconstruction via simultaneous dense and sparse coding

1 citations · 1 across the 2 of their papers we have counts for

collaborators

11 papers

math.OC2026

Provable Non-Convex Euclidean Distance Matrix Completion: Geometry, Reconstruction, and Robustness

Chandler Smith, HanQin Cai, Abiy Tasissa

The problem of recovering the configuration of points from their partial pairwise distances, referred to as the Euclidean Distance Matrix Completion (EDMC) problem, arises in a bro…

cs.IT20261 cited

Discriminative reconstruction via simultaneous dense and sparse coding

Abiy Tasissa, Emmanouil Theodosis, Bahareh Tolooshams +1

Discriminative features extracted from the sparse coding model have been shown to perform well for classification. Recent deep learning architectures have further improved reconstr…

eess.SP2026

Shift-Invariant Feature Attribution in the Application of Wireless Electrocardiograms

Yalemzerf Getnet, Abiy Tasissa, Waltenegus Dargie

Assigning relevance scores to the input features of a machine learning model enables to measure the contributions of the features in achieving a correct outcome. It is regarded as…

stat.ML2025

Recovering Wasserstein Distance Matrices from Few Measurements

Muhammad Rana, Abiy Tasissa, HanQin Cai +2

This paper proposes two algorithms for estimating square Wasserstein distance matrices from a small number of entries. These matrices are used to compute manifold learning embeddin…

eess.SP2025

Robust Node Localization for Rough and Extreme Deployment Environments

Abiy Tasissa, Waltenegus Dargie

Many applications have been identified which require the deployment of large-scale low-power wireless sensor networks. Some of the deployment environments, however, impose harsh op…

cs.LG2025

A Dual Basis Approach for Structured Robust Euclidean Distance Geometry

Chandra Kundu, Abiy Tasissa, HanQin Cai

Euclidean Distance Matrix (EDM), which consists of pairwise squared Euclidean distances of a given point configuration, finds many applications in modern machine learning. This pap…