Search Results for author: Anna C. Gilbert

Found 9 papers, 3 papers with code

May the force be with you

no code implementations13 Aug 2022 Yulan Zhang, Anna C. Gilbert, Stefan Steinerberger

Modern methods in dimensionality reduction are dominated by nonlinear attraction-repulsion force-based methods (this includes t-SNE, UMAP, ForceAtlas2, LargeVis, and many more).

Dimensionality Reduction

How can classical multidimensional scaling go wrong?

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.

Spectral Methods for Ranking with Scarce Data

no code implementations2 Jul 2020 Umang Varma, Lalit Jain, Anna C. Gilbert

In this paper we modify a popular and well studied method, RankCentrality for rank aggregation to account for few comparisons and that incorporates additional feature information.

Tree! I am no Tree! I am a Low Dimensional Hyperbolic Embedding

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.

Project and Forget: Solving Large-Scale Metric Constrained Problems

1 code implementation8 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.

Metric Learning

Project and Forget: Solving Large Scale Metric Constrained Problems

no code implementations25 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.

Metric Learning

Unsupervised Metric Learning in Presence of Missing Data

3 code implementations19 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.

Dimensionality Reduction Matrix Completion +1

If it ain't broke, don't fix it: Sparse metric repair

no code implementations29 Oct 2017 Anna C. Gilbert, Lalit Jain

The distances between the data points are far from satisfying a metric.

Towards Understanding the Invertibility of Convolutional Neural Networks

no code implementations24 May 2017 Anna C. Gilbert, Yi Zhang, Kibok Lee, Yuting Zhang, Honglak Lee

Several recent works have empirically observed that Convolutional Neural Nets (CNNs) are (approximately) invertible.

Compressive Sensing General Classification

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