1 citations · 2 across the 5 of their papers we have counts for
6 papers
On the tradeoff between robustness and geometric accuracy in adversarial training
Rachel Morris, Ryan Murray, Kerrek Stinson
For non-parametric binary classification in the presence of an adversary, we prove an optimal quantitative estimate for adversarial classifiers controlling geometric accuracy, as m…
Large data limits and scaling laws for tSNE
Ryan Murray, Adam Pickarski
This work considers large-data asymptotics for t-distributed stochastic neighbor embedding (tSNE), a widely-used non-linear dimension reduction algorithm. We identify an appropriat…
On Probabilistic Embeddings in Optimal Dimension Reduction
Ryan Murray, Adam Pickarski
Dimension reduction algorithms are a crucial part of many data science pipelines, including data exploration, feature creation and selection, and denoising. Despite their wide util…
Uniform Convergence of Adversarially Robust Classifiers
Rachel Morris, Ryan Murray
In recent years there has been significant interest in the effect of different types of adversarial perturbations in data classification problems. Many of these models incorporate…
Dirichlet Active Learning
Kevin Miller, Ryan Murray
This work introduces Dirichlet Active Learning (DiAL), a Bayesian-inspired approach to the design of active learning algorithms. Our framework models feature-conditional class prob…
Using Skew to Assess the Quality of GAN-generated Image Features
Lorenzo Luzi, Helen Jenne, Ryan Murray +1
The rapid advancement of Generative Adversarial Networks (GANs) necessitates the need to robustly evaluate these models. Among the established evaluation criteria, the FréchetIncep…