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08/13/2022
May the force be with you
Modern methods in dimensionality reduction are dominated by nonlinear at...
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10/21/2021
How can classical multidimensional scaling go wrong?
Given a matrix D describing the pairwise dissimilarities of a data set, ...
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12/05/2020
Dual Regularized Optimal Transport
In this paper, we present a new formulation of unbalanced optimal transp...
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07/02/2020
Spectral Methods for Ranking with Scarce Data
Given a number of pairwise preferences of items, a common task is to ran...
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05/08/2020
Project and Forget: Solving Large-Scale Metric Constrained Problems
Given a set of dissimilarity measurements amongst data points, determini...
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05/08/2020
Tree! I am no Tree! I am a Low Dimensional Hyperbolic Embedding
Given data, finding a faithful low-dimensional hyperbolic embedding of t...
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07/19/2018
Generalized Metric Repair on Graphs
Many modern data analysis algorithms either assume that or are considera...
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07/19/2018
Unrolling Swiss Cheese: Metric repair on manifolds with holes
For many machine learning tasks, the input data lie on a low-dimensional...
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10/29/2017
If it ain't broke, don't fix it: Sparse metric repair
Many modern data-intensive computational problems either require, or ben...
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05/31/2017
Local Differential Privacy for Physical Sensor Data and Sparse Recovery
In this work we explore the utility of locally differentially private th...
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05/24/2017
Towards Understanding the Invertibility of Convolutional Neural Networks
Several recent works have empirically observed that Convolutional Neural...
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02/03/2013