Konstantinos Spiliopoulos

According to our database1, Konstantinos Spiliopoulos authored at least 31 papers between 2008 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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Bibliography

2024
Stochastic gradient descent-based inference for dynamic network models with attractors.
CoRR, 2024

2023
PDE-constrained models with neural network terms: Optimization and global convergence.
J. Comput. Phys., May, 2023

Kernel Limit of Recurrent Neural Networks Trained on Ergodic Data Sequences.
CoRR, 2023

2022
Mean Field Limits of Particle-Based Stochastic Reaction-Diffusion Models.
SIAM J. Math. Anal., 2022

Geometry-informed irreversible perturbations for accelerated convergence of Langevin dynamics.
Stat. Comput., 2022

Mean Field Analysis of Deep Neural Networks.
Math. Oper. Res., 2022

Normalization effects on deep neural networks.
CoRR, 2022

2021
How Reaction-Diffusion PDEs Approximate the Large-Population Limit of Stochastic Particle Models.
SIAM J. Appl. Math., 2021

Discrete-Time Inference for Slow-Fast Systems Driven by Fractional Brownian Motion.
Multiscale Model. Simul., 2021

2020
Mean Field Analysis of Neural Networks: A Law of Large Numbers.
SIAM J. Appl. Math., 2020

Importance Sampling for Slow-Fast Diffusions Based on Moderate Deviations.
Multiscale Model. Simul., 2020

Information Geometry for Approximate Bayesian Computation.
SIAM/ASA J. Uncertain. Quantification, 2020

Selection of Quasi-stationary States in the Stochastically Forced Navier-Stokes Equation on the Torus.
J. Nonlinear Sci., 2020

Normalization effects on shallow neural networks and related asymptotic expansions.
CoRR, 2020

2019
Asymptotics of Reinforcement Learning with Neural Networks.
CoRR, 2019

Scaling Limit of Neural Networks with the Xavier Initialization and Convergence to a Global Minimum.
CoRR, 2019

2018
Analysis of Multiscale Integrators for Multiple Attractors and Irreversible Langevin Samplers.
Multiscale Model. Simul., 2018

Discrete-Time Statistical Inference for Multiscale Diffusions.
Multiscale Model. Simul., 2018

DGM: A deep learning algorithm for solving partial differential equations.
J. Comput. Phys., 2018

2017
Stochastic Gradient Descent in Continuous Time.
SIAM J. Financial Math., 2017

Dimension Reduction in Statistical Estimation of Partially Observed Multiscale Processes.
SIAM/ASA J. Uncertain. Quantification, 2017

Moderate deviations for systems of slow-fast diffusions.
Asymptot. Anal., 2017

2015
Default Clustering in Large Pools: Large Deviations.
SIAM J. Financial Math., 2015

Rare Event Simulation for Multiscale Diffusions in Random Environments.
Multiscale Model. Simul., 2015

Nonasymptotic performance analysis of importance sampling schemes for small noise diffusions.
J. Appl. Probab., 2015

2014
Filtering the Maximum Likelihood for Multiscale Problems.
Multiscale Model. Simul., 2014

Scaling limits and exit law for multiscale diffusions.
Asymptot. Anal., 2014

Rare event simulation in the neighborhood of a rest point.
Proceedings of the 2014 Winter Simulation Conference, 2014

2012
Importance Sampling for Multiscale Diffusions.
Multiscale Model. Simul., 2012

2011
Rare event simulation for rough energy landscapes.
Proceedings of the Winter Simulation Conference 2011, 2011

2008
Reaction-diffusion equations with nonlinear boundary conditions in narrow domains.
Asymptot. Anal., 2008


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