Konstantinos C. Zygalakis
Orcid: 0000-0002-3860-9167
According to our database1,
Konstantinos C. Zygalakis
authored at least 44 papers
between 2009 and 2024.
Collaborative distances:
Collaborative distances:
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Bibliography
2024
Pattern Recognit., March, 2024
IEEE Signal Process. Lett., 2024
A hybrid tau-leap for simulating chemical kinetics with applications to parameter estimation.
CoRR, 2024
2023
The Split Gibbs Sampler Revisited: Improvements to Its Algorithmic Structure and Augmented Target Distribution.
SIAM J. Imaging Sci., December, 2023
SIAM J. Imaging Sci., September, 2023
Stat. Comput., August, 2023
Backward error analysis and the qualitative behaviour of stochastic optimization algorithms: Application to stochastic coordinate descent.
CoRR, 2023
Gaussian processes for Bayesian inverse problems associated with linear partial differential equations.
CoRR, 2023
On the connections between optimization algorithms, Lyapunov functions, and differential equations: theory and insights.
CoRR, 2023
Introduction To Gaussian Process Regression In Bayesian Inverse Problems, With New ResultsOn Experimental Design For Weighted Error Measures.
CoRR, 2023
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023
2022
Multiscale Model. Simul., December, 2022
SIAM J. Imaging Sci., June, 2022
2021
The Connections Between Lyapunov Functions for Some Optimization Algorithms and Differential Equations.
SIAM J. Numer. Anal., 2021
Wasserstein distance estimates for the distributions of numerical approximations to ergodic stochastic differential equations.
J. Mach. Learn. Res., 2021
Bayesian Imaging With Data-Driven Priors Encoded by Neural Networks: Theory, Methods, and Algorithms.
CoRR, 2021
2020
SIAM J. Numer. Anal., 2020
Accelerating Proximal Markov Chain Monte Carlo by Using an Explicit Stabilized Method.
SIAM J. Imaging Sci., 2020
Multi-level Monte Carlo methods for the approximation of invariant measures of stochastic differential equations.
Stat. Comput., 2020
Implicit Regularization in Matrix Sensing: A Geometric View Leads to Stronger Results.
CoRR, 2020
2019
CoRR, 2019
2018
SIAM/ASA J. Uncertain. Quantification, 2018
2017
Statistical analysis of differential equations: introducing probability measures on numerical solutions.
Stat. Comput., 2017
CoRR, 2017
2016
Exploration of the (Non-)Asymptotic Bias and Variance of Stochastic Gradient Langevin Dynamics.
J. Mach. Learn. Res., 2016
J. Comput. Phys., 2016
Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016
2015
SIAM J. Numer. Anal., 2015
2014
SIAM J. Numer. Anal., 2014
Efficient simulation of stochastic chemical kinetics with the Stochastic Bulirsch-Stoer extrapolation method.
BMC Syst. Biol., 2014
2013
Weak Second Order Explicit Stabilized Methods for Stiff Stochastic Differential Equations.
SIAM J. Sci. Comput., 2013
2012
High Weak Order Methods for Stochastic Differential Equations Based on Modified Equations.
SIAM J. Sci. Comput., 2012
Multiscale Model. Simul., 2012
A higher-order numerical framework for stochastic simulation of chemical reaction systems.
BMC Syst. Biol., 2012
2011
On the Existence and the Applications of Modified Equations for Stochastic Differential Equations.
SIAM J. Sci. Comput., 2011
SIAM J. Appl. Math., 2011
2009
J. Comput. Phys., 2009