Algebraic Decay to Equilibrium for the Becker-Döring Equations. - Ryan W. Murray; Robert L. Pego (SIAM J. Math. Anal., 2016)

Fictitious Play in Potential Games. - Brian Swenson; Ryan W. Murray; Soummya Kar (CoRR, 2017)

Best-Response Dynamics in Continuous Potential Games: Non-Convergence to Saddle Points. - Brian Swenson; Ryan W. Murray; Soummya Kar; H. Vincent Poor (ACSSC, 2018)

A model for system uncertainty in reinforcement learning. - Ryan W. Murray; Michele Palladino (Syst. Control. Lett., 2018)

On Best-Response Dynamics in Potential Games. - Brian Swenson; Ryan W. Murray; Soummya Kar (SIAM J. Control. Optim., 2018)

Distributed Gradient Descent: Nonconvergence to Saddle Points and the Stable-Manifold Theorem. - Brian Swenson; Ryan W. Murray; H. Vincent Poor; Soummya Kar (Allerton, 2019)

Modelling uncertainty in reinforcement learning. - Ryan W. Murray; Michele Palladino (CDC, 2019)

Revisiting Normalized Gradient Descent: Fast Evasion of Saddle Points. - Ryan W. Murray; Brian Swenson; Soummya Kar (IEEE Trans. Autom. Control., 2019)

From graph cuts to isoperimetric inequalities: Convergence rates of Cheeger cuts on data clouds. - Nicolás García Trillos; Ryan Murray 001; Matthew Thorpe (CoRR, 2020)

Regular potential games. - Brian Swenson; Ryan W. Murray; Soummya Kar (Games Econ. Behav., 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)

The Geometry of Adversarial Training in Binary Classification. - Leon Bungert; Nicolás García Trillos; Ryan Murray 001 (CoRR, 2021)

Eikonal depth: an optimal control approach to statistical depths. - Martin Molina-Fructuoso; Ryan W. Murray (CoRR, 2022)

Rates of Convergence for Regression with the Graph Poly-Laplacian. - Nicolás García Trillos; Ryan Murray 001; Matthew Thorpe (CoRR, 2022)

Distributed Stochastic Gradient Descent: Nonconvexity, Nonsmoothness, and Convergence to Local Minima. - Brian Swenson; Ryan Murray 001; H. Vincent Poor; Soummya Kar (J. Mach. Learn. Res., 2022)

Adversarial Classification: Necessary Conditions and Geometric Flows. - Nicolás García Trillos; Ryan Murray 001 (J. Mach. Learn. Res., 2022)

Tukey Depths and Hamilton-Jacobi Differential Equations. - Martin Molina-Fructuoso; Ryan W. Murray (SIAM J. Math. Data Sci., 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)

Using Higher-Order Moments to Assess the Quality of GAN-generated Image Features. - Lorenzo Luzi; Helen Jenne; Ryan Murray 001; Carlos Ortiz Marrero (CoRR, 2023)

Dirichlet Active Learning. - Kevin S. Miller; Ryan Murray 001 (CoRR, 2023)

Uniform Convergence of Adversarially Robust Classifiers. - Rachel Morris; Ryan Murray 001 (CoRR, 2024)

On Probabilistic Embeddings in Optimal Dimension Reduction. - Ryan Murray 001; Adam Pickarski (CoRR, 2024)

Large data limits and scaling laws for tSNE. - Ryan Murray 001; Adam Pickarski (CoRR, 2024)

Using Skew to Assess the Quality of GAN-generated Image Features. - Lorenzo Luzi; Helen Jenne; Carlos Ortiz Marrero; Ryan Murray 001 (Trans. Mach. Learn. Res., 2024)