19 citations · 54 across the 25 of their papers we have counts for
8 papers · 1 filter
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…
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…
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…
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…
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…
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…
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.…