Ryan Murray

14 papers Journal 14
YearRankTypeTitle / Venue / Authors
2025 J jnl
On Probabilistic Embeddings in Optimal Dimension Reduction.
J. Mach. Learn. Res.
Ryan Murray, Adam Pickarski
2024 J jnl
Large data limits and scaling laws for tSNE.
CoRR
Ryan Murray, Adam Pickarski
2024 J jnl
On Probabilistic Embeddings in Optimal Dimension Reduction.
CoRR
Ryan Murray, Adam Pickarski
2024 J jnl
Uniform Convergence of Adversarially Robust Classifiers.
CoRR
Rachel Morris, Ryan Murray
2024 J jnl
Using Skew to Assess the Quality of GAN-generated Image Features.
Trans. Mach. Learn. Res.
Lorenzo Luzi, Helen Jenne, Carlos Ortiz Marrero, Ryan Murray
2023 J jnl
Dirichlet Active Learning.
CoRR
Kevin S. Miller, Ryan Murray
2023 J jnl
Using Higher-Order Moments to Assess the Quality of GAN-generated Image Features.
CoRR
Lorenzo Luzi, Helen Jenne, Ryan Murray, Carlos Ortiz Marrero
2022 J jnl
Adversarial Classification: Necessary Conditions and Geometric Flows.
J. Mach. Learn. Res.
Nicolás García Trillos, Ryan Murray
2022 J jnl
Distributed Stochastic Gradient Descent: Nonconvexity, Nonsmoothness, and Convergence to Local Minima.
J. Mach. Learn. Res.
Brian Swenson, Ryan Murray, H. Vincent Poor, Soummya Kar
2022 J jnl
Rates of Convergence for Regression with the Graph Poly-Laplacian.
CoRR
Nicolás García Trillos, Ryan Murray, Matthew Thorpe
2021 J jnl
The Geometry of Adversarial Training in Binary Classification.
CoRR
Leon Bungert, Nicolás García Trillos, Ryan Murray
2020 J jnl
Adversarial Classification: Necessary conditions and geometric flows.
CoRR
Nicolás García Trillos, Ryan Murray
2020 J jnl
From graph cuts to isoperimetric inequalities: Convergence rates of Cheeger cuts on data clouds.
CoRR
Nicolás García Trillos, Ryan Murray, Matthew Thorpe
2019 J jnl
A maximum principle argument for the uniform convergence of graph Laplacian regressors.
CoRR
Nicolás García Trillos, Ryan Murray