Ryan Murray

According to our database, Ryan Murray authored at least 25 papers between 2016 and 2026.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2026

On the continuum limit of t-SNE for data visualization.

Jeff Calder, Zhonggan Huang, Ryan Murray, Adam Pickarski

CoRR, April, 2026

2025

On Probabilistic Embeddings in Optimal Dimension Reduction.

Ryan Murray, Adam Pickarski

J. Mach. Learn. Res., 2025

2024

Using Skew to Assess the Quality of GAN-generated Image Features.

Lorenzo Luzi, Helen Jenne, Carlos Ortiz Marrero, Ryan Murray

Trans. Mach. Learn. Res., 2024

Large data limits and scaling laws for tSNE.

Ryan Murray, Adam Pickarski

CoRR, 2024

Uniform Convergence of Adversarially Robust Classifiers.

Rachel Morris, Ryan Murray

CoRR, 2024

2023

Dirichlet Active Learning.

Kevin S. Miller, Ryan Murray

CoRR, 2023

Using Higher-Order Moments to Assess the Quality of GAN-generated Image Features.

Lorenzo Luzi, Helen Jenne, Ryan Murray, Carlos Ortiz Marrero

CoRR, 2023

2022

Distributed Gradient Flow: Nonsmoothness, Nonconvexity, and Saddle Point Evasion.

Brian Swenson, Ryan W. Murray, H. Vincent Poor, Soummya Kar

IEEE Trans. Autom. Control., 2022

Tukey Depths and Hamilton-Jacobi Differential Equations.

Martin Molina-Fructuoso, Ryan W. Murray

SIAM J. Math. Data Sci., 2022

Adversarial Classification: Necessary Conditions and Geometric Flows.

Nicolás García Trillos, Ryan Murray

J. Mach. Learn. Res., 2022

Distributed Stochastic Gradient Descent: Nonconvexity, Nonsmoothness, and Convergence to Local Minima.

Brian Swenson, Ryan Murray, H. Vincent Poor, Soummya Kar

J. Mach. Learn. Res., 2022

Rates of Convergence for Regression with the Graph Poly-Laplacian.

Nicolás García Trillos, Ryan Murray, Matthew Thorpe

CoRR, 2022

Eikonal depth: an optimal control approach to statistical depths.

Martin Molina-Fructuoso, Ryan W. Murray

CoRR, 2022

2021

The Geometry of Adversarial Training in Binary Classification.

Leon Bungert, Nicolás García Trillos, Ryan Murray

CoRR, 2021

2020

A Maximum Principle Argument for the Uniform Convergence of Graph Laplacian Regressors.

Nicolás García Trillos, Ryan W. Murray

SIAM J. Math. Data Sci., 2020

Regular potential games.

Brian Swenson, Ryan W. Murray, Soummya Kar

Games Econ. Behav., 2020

From graph cuts to isoperimetric inequalities: Convergence rates of Cheeger cuts on data clouds.

Nicolás García Trillos, Ryan Murray, Matthew Thorpe

CoRR, 2020

2019

Revisiting Normalized Gradient Descent: Fast Evasion of Saddle Points.

Ryan W. Murray, Brian Swenson, Soummya Kar

IEEE Trans. Autom. Control., 2019

Modelling uncertainty in reinforcement learning.

Ryan W. Murray, Michele Palladino

Proceedings of the 58th IEEE Conference on Decision and Control, 2019

Distributed Gradient Descent: Nonconvergence to Saddle Points and the Stable-Manifold Theorem.

Brian Swenson, Ryan W. Murray, H. Vincent Poor, Soummya Kar

Proceedings of the 57th Annual Allerton Conference on Communication, 2019

2018

On Best-Response Dynamics in Potential Games.

Brian Swenson, Ryan W. Murray, Soummya Kar

SIAM J. Control. Optim., 2018

A model for system uncertainty in reinforcement learning.

Ryan W. Murray, Michele Palladino

Syst. Control. Lett., 2018

Best-Response Dynamics in Continuous Potential Games: Non-Convergence to Saddle Points.

Brian Swenson, Ryan W. Murray, Soummya Kar, H. Vincent Poor

Proceedings of the 52nd Asilomar Conference on Signals, Systems, and Computers, 2018

2017

Fictitious Play in Potential Games.

Brian Swenson, Ryan W. Murray, Soummya Kar

CoRR, 2017

2016

Algebraic Decay to Equilibrium for the Becker-Döring Equations.

Ryan W. Murray, Robert L. Pego

SIAM J. Math. Anal., 2016