19 citations · 54 across the 25 of their papers we have counts for
28 papers
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…
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…
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…
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…
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…
Prediction of the Received Power of Low-Power Networks Using Inertial Sensors
Waltenegus Dargie, Christian Poellabauer, Abiy Tasissa
Low-power and cost-effective IoT sensing nodes enable scalable monitoring of different environments. Some of these environments impose rough and extreme operating conditions, requi…
Structured Sampling for Robust Euclidean Distance Geometry
Chandra Kundu, Abiy Tasissa, HanQin Cai
This paper addresses the problem of estimating the positions of points from distance measurements corrupted by sparse outliers. Specifically, we consider a setting with two types o…
Sample-Efficient Geometry Reconstruction from Euclidean Distances using Non-Convex Optimization
Ipsita Ghosh, Abiy Tasissa, Christian Kümmerle
The problem of finding suitable point embedding or geometric configurations given only Euclidean distance information of point pairs arises both as a core task and as a sub-problem…
Riemannian Optimization for Non-convex Euclidean Distance Geometry with Global Recovery Guarantees
Chandler Smith, HanQin Cai, Abiy Tasissa
The problem of determining the configuration of points from partial distance information, known as the Euclidean Distance Geometry (EDG) problem, is fundamental to many tasks in th…
Locality Regularized Reconstruction: Structured Sparsity and Delaunay Triangulations
Marshall Mueller, James M. Murphy, Abiy Tasissa
Linear representation learning is widely studied due to its conceptual simplicity and empirical utility in tasks such as compression, classification, and feature extraction. Given…
Clustering Inductive Biases with Unrolled Networks
Jonathan Huml, Abiy Tasissa, Demba Ba
The classical sparse coding (SC) model represents visual stimuli as a linear combination of a handful of learned basis functions that are Gabor-like when trained on natural image d…
Localization from structured distance matrices via low-rank matrix recovery
Samuel Lichtenberg, Abiy Tasissa
We study the problem of determining the configuration of points by using their distances to nodes, referred to as anchor nodes. One sampling scheme is Nystrom sampling, whi…
RACH-Space: Reconstructing Adaptive Convex Hull Space with Applications in Weak Supervision
Woojoo Na, Abiy Tasissa
We introduce RACH-Space, an algorithm for labelling unlabelled data in weakly supervised learning, given incomplete, noisy information about the labels. RACH-Space offers simplicit…
A dual basis approach to multidimensional scaling
Samuel Lichtenberg, Abiy Tasissa
Classical multidimensional scaling (CMDS) is a technique that embeds a set of objects in a Euclidean space given their pairwise Euclidean distances. The main part of CMDS involves…
Sparse, Geometric Autoencoder Models of V1
Jonathan Huml, Abiy Tasissa, Demba Ba
The classical sparse coding model represents visual stimuli as a linear combination of a handful of learned basis functions that are Gabor-like when trained on natural image data.…
SHAPER: Can You Hear the Shape of a Jet?
Demba Ba, Akshunna S. Dogra, Rikab Gambhir +2
The identification of interesting substructures within jets is an important tool for searching for new physics and probing the Standard Model at colliders. Many of these substructu…
Alternating minimization algorithm with initialization analysis for r-local and k-sparse unlabeled sensing
Ahmed Abbasi, Shuchin Aeron, Abiy Tasissa
Unlabeled sensing is a linear inverse problem with permuted measurements. We propose an alternating minimization (AltMin) algorithm with a suitable initialization for two widely co…
Geometric Sparse Coding in Wasserstein Space
Marshall Mueller, Shuchin Aeron, James M. Murphy +1
Wasserstein dictionary learning is an unsupervised approach to learning a collection of probability distributions that generate observed distributions as Wasserstein barycentric co…
Measure Estimation in the Barycentric Coding Model
Matthew Werenski, Ruijie Jiang, Abiy Tasissa +2
This paper considers the problem of measure estimation under the barycentric coding model (BCM), in which an unknown measure is assumed to belong to the set of Wasserstein-2 baryce…
r-local sensing: Improved algorithm and applications
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. We study a particular case of the problem where the perm…
Weighed l1 on the simplex: Compressive sensing meets locality
Abiy Tasissa, Pranay Tankala, Demba Ba
Sparse manifold learning algorithms combine techniques in manifold learning and sparse optimization to learn features that could be utilized for downstream tasks. The standard sett…
On the convergence of group-sparse autoencoders
Emmanouil Theodosis, Bahareh Tolooshams, Pranay Tankala +2
Recent approaches in the theoretical analysis of model-based deep learning architectures have studied the convergence of gradient descent in shallow ReLU networks that arise from g…
Deep Diffusion Processes for Active Learning of Hyperspectral Images
Abiy Tasissa, Duc Nguyen, James Murphy
A method for active learning of hyperspectral images (HSI) is proposed, which combines deep learning with diffusion processes on graphs. A deep variational autoencoder extracts smo…
K-Deep Simplex: Deep Manifold Learning via Local Dictionaries
Pranay Tankala, Abiy Tasissa, James M. Murphy +1
We propose K-Deep Simplex(KDS) which, given a set of data points, learns a dictionary comprising synthetic landmarks, along with representation coefficients supported on a simplex.…