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no code implementations • NeurIPS 2021 • Rishi Sonthalia, Gregory Van Buskirk, Benjamin Raichel, Anna C. Gilbert

While $D_l$ is not metric, when given as input to cMDS instead of $D$, it empirically results in solutions whose distance to $D$ does not increase when we increase the dimension and the classification accuracy degrades less than the cMDS solution.

no code implementations • 10 Oct 2021 • Dominic Flocco, Bryce Palmer-Toy, Ruixiao Wang, Hongyu Zhu, Rishi Sonthalia, Junyuan Lin, Andrea L. Bertozzi, P. Jeffrey Brantingham

The construction and application of knowledge graphs have seen a rapid increase across many disciplines in recent years.

no code implementations • 29 Sep 2021 • Rishi Sonthalia, Raj Rao Nadakuditi

In fact the generalization error versus number of of training data points is a double descent curve.

2 code implementations • NeurIPS 2020 • Rishi Sonthalia, Anna C. Gilbert

Given data, finding a faithful low-dimensional hyperbolic embedding of the data is a key method by which we can extract hierarchical information or learn representative geometric features of the data.

1 code implementation • 8 May 2020 • Rishi Sonthalia, Anna C. Gilbert

Given a set of dissimilarity measurements amongst data points, determining what metric representation is most "consistent" with the input measurements or the metric that best captures the relevant geometric features of the data is a key step in many machine learning algorithms.

no code implementations • 25 Sep 2019 • Anna C. Gilbert, Rishi Sonthalia

Given a set of distances amongst points, determining what metric representation is most “consistent” with the input distances or the metric that captures the relevant geometric features of the data is a key step in many machine learning algorithms.

3 code implementations • 19 Jul 2018 • Anna C. Gilbert, Rishi Sonthalia

Here, we present a new algorithm MR-MISSING that extends these previous algorithms and can be used to compute low dimensional representation on data sets with missing entries.

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