Reaction-diffusion equations with nonlinear boundary conditions in narrow domains. - Mark Freidlin; Konstantinos Spiliopoulos (Asymptot. Anal., 2008)

Rare event simulation for rough energy landscapes. - Paul Dupuis; Konstantinos Spiliopoulos; Hui Wang 003 (WSC, 2011)

Importance Sampling for Multiscale Diffusions. - Paul Dupuis; Konstantinos Spiliopoulos; Hui Wang 003 (Multiscale Model. Simul., 2012)

Rare event simulation in the neighborhood of a rest point. - Paul Dupuis; Konstantinos Spiliopoulos (WSC, 2014)

Scaling limits and exit law for multiscale diffusions. - Sergio Angel Almada; Konstantinos Spiliopoulos (Asymptot. Anal., 2014)

Filtering the Maximum Likelihood for Multiscale Problems. - Andrew Papanicolaou; Konstantinos Spiliopoulos (Multiscale Model. Simul., 2014)

Nonasymptotic performance analysis of importance sampling schemes for small noise diffusions. - Konstantinos Spiliopoulos (J. Appl. Probab., 2015)

Rare Event Simulation for Multiscale Diffusions in Random Environments. - Konstantinos Spiliopoulos (Multiscale Model. Simul., 2015)

Default Clustering in Large Pools: Large Deviations. - Konstantinos Spiliopoulos; Richard B. Sowers (SIAM J. Financial Math., 2015)

Moderate deviations for systems of slow-fast diffusions. - Matthew R. Morse; Konstantinos Spiliopoulos (Asymptot. Anal., 2017)

Dimension Reduction in Statistical Estimation of Partially Observed Multiscale Processes. - Andrew Papanicolaou; Konstantinos Spiliopoulos (SIAM/ASA J. Uncertain. Quantification, 2017)

Stochastic Gradient Descent in Continuous Time. - Justin A. Sirignano; Konstantinos Spiliopoulos (SIAM J. Financial Math., 2017)

DGM: A deep learning algorithm for solving partial differential equations. - Justin A. Sirignano; Konstantinos Spiliopoulos (J. Comput. Phys., 2018)

Discrete-Time Statistical Inference for Multiscale Diffusions. - Siragan Gailus; Konstantinos Spiliopoulos (Multiscale Model. Simul., 2018)

Analysis of Multiscale Integrators for Multiple Attractors and Irreversible Langevin Samplers. - Jianfeng Lu 001; Konstantinos Spiliopoulos (Multiscale Model. Simul., 2018)

Scaling Limit of Neural Networks with the Xavier Initialization and Convergence to a Global Minimum. - Justin A. Sirignano; Konstantinos Spiliopoulos (CoRR, 2019)

Asymptotics of Reinforcement Learning with Neural Networks. - Justin A. Sirignano; Konstantinos Spiliopoulos (CoRR, 2019)

Normalization effects on shallow neural networks and related asymptotic expansions. - Jiahui Yu; Konstantinos Spiliopoulos (CoRR, 2020)

Selection of Quasi-stationary States in the Stochastically Forced Navier-Stokes Equation on the Torus. - Margaret Beck; Eric Cooper; Gabriel J. Lord; Konstantinos Spiliopoulos (J. Nonlinear Sci., 2020)

Information Geometry for Approximate Bayesian Computation. - Konstantinos Spiliopoulos (SIAM/ASA J. Uncertain. Quantification, 2020)

Importance Sampling for Slow-Fast Diffusions Based on Moderate Deviations. - Konstantinos Spiliopoulos; Matthew R. Morse (Multiscale Model. Simul., 2020)

Mean Field Analysis of Neural Networks: A Law of Large Numbers. - Justin A. Sirignano; Konstantinos Spiliopoulos (SIAM J. Appl. Math., 2020)

Discrete-Time Inference for Slow-Fast Systems Driven by Fractional Brownian Motion. - Solesne Bourguin; Siragan Gailus; Konstantinos Spiliopoulos (Multiscale Model. Simul., 2021)

How Reaction-Diffusion PDEs Approximate the Large-Population Limit of Stochastic Particle Models. - Samuel A. Isaacson; Jingwei Ma; Konstantinos Spiliopoulos (SIAM J. Appl. Math., 2021)

Normalization effects on deep neural networks. - Jiahui Yu; Konstantinos Spiliopoulos (CoRR, 2022)

Mean Field Analysis of Deep Neural Networks. - Justin A. Sirignano; Konstantinos Spiliopoulos (Math. Oper. Res., 2022)

Geometry-informed irreversible perturbations for accelerated convergence of Langevin dynamics. - Benjamin J. Zhang; Youssef M. Marzouk; Konstantinos Spiliopoulos (Stat. Comput., 2022)

Mean Field Limits of Particle-Based Stochastic Reaction-Diffusion Models. - Samuel A. Isaacson; Jingwei Ma; Konstantinos Spiliopoulos (SIAM J. Math. Anal., 2022)

Kernel Limit of Recurrent Neural Networks Trained on Ergodic Data Sequences. - Samuel Chun-Hei Lam; Justin A. Sirignano; Konstantinos Spiliopoulos (CoRR, 2023)

PDE-constrained models with neural network terms: Optimization and global convergence. - Justin A. Sirignano; Jonathan F. MacArt; Konstantinos Spiliopoulos (J. Comput. Phys., 2023)

Stochastic gradient descent-based inference for dynamic network models with attractors. - Hancong Pan; Xiaojing Zhu; Cantay Caliskan; Dino P. Christenson; Konstantinos Spiliopoulos; Dylan Walker; Eric D. Kolaczyk (CoRR, 2024)

Convergence Analysis of Real-time Recurrent Learning (RTRL) for a class of Recurrent Neural Networks. - Samuel Chun-Hei Lam; Justin A. Sirignano; Konstantinos Spiliopoulos (CoRR, 2025)

Global Convergence of Adjoint-Optimized Neural PDEs. - Konstantin Riedl; Justin A. Sirignano; Konstantinos Spiliopoulos (CoRR, 2025)