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Konstantinos C. Zygalakis
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2020 – today
- 2024
- [j28]Oliver M. Crook, Mihai Cucuringu, Tim Hurst, Carola-Bibiane Schönlieb, Matthew Thorpe, Konstantinos C. Zygalakis:
A linear transportation Lp distance for pattern recognition. Pattern Recognit. 147: 110080 (2024) - [j27]Tianming Bai, Aretha L. Teckentrup, Konstantinos C. Zygalakis:
Gaussian processes for Bayesian inverse problems associated with linear partial differential equations. Stat. Comput. 34(4): 139 (2024) - [j26]Teresa Klatzer
, Paul Dobson, Yoann Altmann
Accelerated Bayesian Imaging by Relaxed Proximal-Point Langevin Sampling. SIAM J. Imaging Sci. 17(2): 1078-1117 (2024) - [j25]Savvas Melidonis
Empirical Bayesian Imaging With Large-Scale Push-Forward Generative Priors. IEEE Signal Process. Lett. 31: 631-635 (2024) - [i24]Thomas Trigo Trindade, Konstantinos C. Zygalakis:
A hybrid tau-leap for simulating chemical kinetics with applications to parameter estimation. CoRR abs/2401.09097 (2024) - [i23]Xue Gong, Desmond J. Higham, Konstantinos Zygalakis, Ginestra Bianconi:
Higher-order Connection Laplacians for Directed Simplicial Complexes. CoRR abs/2402.07631 (2024) - [i22]Abdul-Lateef Haji-Ali, Marcelo Pereyra, Luke Shaw, Konstantinos Zygalakis:
Bayesian computation with generative diffusion models by Multilevel Monte Carlo. CoRR abs/2409.15511 (2024) - 2023
- [j24]Armin Eftekhari, Luis Vargas, Konstantinos C. Zygalakis
The forward-backward envelope for sampling with the overdamped Langevin algorithm. Stat. Comput. 33(4): 85 (2023) - [j23]Savvas Melidonis
Efficient Bayesian Computation for Low-Photon Imaging Problems. SIAM J. Imaging Sci. 16(3): 1195-1234 (2023) - [j22]Marcelo Pereyra
The Split Gibbs Sampler Revisited: Improvements to Its Algorithmic Structure and Augmented Target Distribution. SIAM J. Imaging Sci. 16(4): 2040-2071 (2023) - [c2]Hoomaan Maskan, Konstantinos Zygalakis, Alp Yurtsever:
A Variational Perspective on High-Resolution ODEs. NeurIPS 2023 - [i21]Tapio Helin, Andrew M. Stuart, Aretha L. Teckentrup, Konstantinos Zygalakis:
Introduction To Gaussian Process Regression In Bayesian Inverse Problems, With New ResultsOn Experimental Design For Weighted Error Measures. CoRR abs/2302.04518 (2023) - [i20]Paul Dobson, Jesús María Sanz-Serna, Konstantinos Zygalakis:
On the connections between optimization algorithms, Lyapunov functions, and differential equations: theory and insights. CoRR abs/2305.08658 (2023) - [i19]Tianming Bai, Aretha L. Teckentrup, Konstantinos C. Zygalakis:
Gaussian processes for Bayesian inverse problems associated with linear partial differential equations. CoRR abs/2307.08343 (2023) - [i18]Teresa Klatzer, Paul Dobson, Yoann Altmann
Accelerated Bayesian imaging by relaxed proximal-point Langevin sampling. CoRR abs/2308.09460 (2023) - [i17]Stefano Di Giovacchino, Desmond J. Higham, Konstantinos Zygalakis:
Backward error analysis and the qualitative behaviour of stochastic optimization algorithms: Application to stochastic coordinate descent. CoRR abs/2309.02082 (2023) - [i16]Hoomaan Maskan, Konstantinos C. Zygalakis, Alp Yurtsever:
A Variational Perspective on High-Resolution ODEs. CoRR abs/2311.02002 (2023) - 2022
- [j21]Stefano Di Giovacchino, Desmond J. Higham, Konstantinos C. Zygalakis
A Hierarchy of Network Models Giving Bistability Under Triadic Closure. Multiscale Model. Simul. 20(4): 1394-1410 (2022) - [j20]Matthew Holden, Marcelo Pereyra
Bayesian Imaging with Data-Driven Priors Encoded by Neural Networks. SIAM J. Imaging Sci. 15(2): 892-924 (2022) - [i15]Armin Eftekhari, Luis Vargas, Konstantinos Zygalakis:
The Forward-Backward Envelope for Sampling with the Overdamped Langevin Algorithm. CoRR abs/2201.09096 (2022) - [i14]Xue Gong, Desmond J. Higham, Konstantinos Zygalakis:
Generative Hypergraph Models and Spectral Embedding. CoRR abs/2207.13895 (2022) - [i13]Enrico Crovini, Simon L. Cotter, Konstantinos Zygalakis, Andrew B. Duncan:
Batch Bayesian Optimization via Particle Gradient Flows. CoRR abs/2209.04722 (2022) - 2021
- [j19]Jesús María Sanz-Serna, Konstantinos C. Zygalakis:
Wasserstein distance estimates for the distributions of numerical approximations to ergodic stochastic differential equations. J. Mach. Learn. Res. 22: 242:1-242:37 (2021) - [j18]Jesús María Sanz-Serna, Konstantinos C. Zygalakis
The Connections Between Lyapunov Functions for Some Optimization Algorithms and Differential Equations. SIAM J. Numer. Anal. 59(3): 1542-1565 (2021) - [i12]Matthew Holden, Marcelo Pereyra, Konstantinos C. Zygalakis:
Bayesian Imaging With Data-Driven Priors Encoded by Neural Networks: Theory, Methods, and Algorithms. CoRR abs/2103.10182 (2021) - [i11]Jesús María Sanz-Serna, Konstantinos C. Zygalakis:
Wasserstein distance estimates for the distributions of numerical approximations to ergodic stochastic differential equations. CoRR abs/2104.12384 (2021) - [i10]Xue Gong
Directed Network Laplacians and Random Graph Models. CoRR abs/2107.03026 (2021) - [i9]Stefano Di Giovacchino, Desmond J. Higham, Konstantinos C. Zygalakis:
A Hierarchy of Network Models Giving Bistability Under Triadic Closure. CoRR abs/2111.05715 (2021) - 2020
- [j17]Michael B. Giles
Multi-level Monte Carlo methods for the approximation of invariant measures of stochastic differential equations. Stat. Comput. 30(3): 507-524 (2020) - [j16]Marcelo Pereyra
Accelerating Proximal Markov Chain Monte Carlo by Using an Explicit Stabilized Method. SIAM J. Imaging Sci. 13(2): 905-935 (2020) - [j15]Jesús María Sanz-Serna, Konstantinos C. Zygalakis
Contractivity of Runge-Kutta Methods for Convex Gradient Systems. SIAM J. Numer. Anal. 58(4): 2079-2092 (2020) - [i8]Armin Eftekhari, Konstantinos Zygalakis:
Implicit Regularization in Matrix Sensing: A Geometric View Leads to Stronger Results. CoRR abs/2008.12091 (2020) - [i7]Jesús María Sanz-Serna, Konstantinos C. Zygalakis:
The connections between Lyapunov functions for some optimization algorithms and differential equations. CoRR abs/2009.00673 (2020) - [i6]Oliver M. Crook, Mihai Cucuringu, Tim Hurst, Carola-Bibiane Schönlieb, Matthew Thorpe, Konstantinos C. Zygalakis:
A Linear Transportation Lp Distance for Pattern Recognition. CoRR abs/2009.11262 (2020)
2010 – 2019
- 2019
- [i5]Luis Vargas
Accelerating proximal Markov chain Monte Carlo by using explicit stabilised methods. CoRR abs/1908.08845 (2019) - [i4]Jesús María Sanz-Serna, Konstantinos C. Zygalakis:
Contractivity of Runge-Kutta methods for convex gradient systems. CoRR abs/1909.09971 (2019) - [i3]Oliver M. Crook, Tim Hurst, Carola-Bibiane Schönlieb, Matthew Thorpe, Konstantinos C. Zygalakis:
PDE-Inspired Algorithms for Semi-Supervised Learning on Point Clouds. CoRR abs/1909.10221 (2019) - 2018
- [j14]Andrea L. Bertozzi
Uncertainty Quantification in Graph-Based Classification of High Dimensional Data. SIAM/ASA J. Uncertain. Quantification 6(2): 568-595 (2018) - 2017
- [j13]Patrick R. Conrad, Mark A. Girolami
Statistical analysis of differential equations: introducing probability measures on numerical solutions. Stat. Comput. 27(4): 1065-1082 (2017) - [i2]Andrea L. Bertozzi, Xiyang Luo, Andrew M. Stuart, Konstantinos C. Zygalakis:
Uncertainty Quantification in the Classification of High Dimensional Data. CoRR abs/1703.08816 (2017) - 2016
- [j12]Andrew B. Duncan, Radek Erban, Konstantinos C. Zygalakis
Hybrid framework for the simulation of stochastic chemical kinetics. J. Comput. Phys. 326: 398-419 (2016) - [j11]Sebastian J. Vollmer, Konstantinos C. Zygalakis, Yee Whye Teh:
Exploration of the (Non-)Asymptotic Bias and Variance of Stochastic Gradient Langevin Dynamics. J. Mach. Learn. Res. 17: 159:1-159:48 (2016) - [c1]Onur Teymur, Konstantinos Zygalakis, Ben Calderhead:
Probabilistic Linear Multistep Methods. NIPS 2016: 4314-4321 - [i1]Onur Teymur, Konstantinos Zygalakis, Ben Calderhead:
Probabilistic Linear Multistep Methods. CoRR abs/1610.08417 (2016) - 2015
- [j10]Assyr Abdulle, Gilles Vilmart
Long Time Accuracy of Lie-Trotter Splitting Methods for Langevin Dynamics. SIAM J. Numer. Anal. 53(1): 1-16 (2015) - 2014
- [j9]Tamás Székely
Efficient simulation of stochastic chemical kinetics with the Stochastic Bulirsch-Stoer extrapolation method. BMC Syst. Biol. 8: 71 (2014) - [j8]Assyr Abdulle, Gilles Vilmart, Konstantinos C. Zygalakis
High Order Numerical Approximation of the Invariant Measure of Ergodic SDEs. SIAM J. Numer. Anal. 52(4): 1600-1622 (2014) - 2013
- [j7]Assyr Abdulle, Gilles Vilmart
Weak Second Order Explicit Stabilized Methods for Stiff Stochastic Differential Equations. SIAM J. Sci. Comput. 35(4) (2013) - 2012
- [j6]Tamás Székely
A higher-order numerical framework for stochastic simulation of chemical reaction systems. BMC Syst. Biol. 6: 85 (2012) - [j5]Gautam Iyer
Numerical Studies of Homogenization under a Fast Cellular Flow. Multiscale Model. Simul. 10(3): 1046-1058 (2012) - [j4]Assyr Abdulle, David Cohen
High Weak Order Methods for Stochastic Differential Equations Based on Modified Equations. SIAM J. Sci. Comput. 34(3) (2012) - 2011
- [j3]Jana Lipková, Konstantinos C. Zygalakis
Analysis of Brownian Dynamics Simulations of Reversible Bimolecular Reactions. SIAM J. Appl. Math. 71(3): 714-730 (2011) - [j2]Konstantinos C. Zygalakis
On the Existence and the Applications of Modified Equations for Stochastic Differential Equations. SIAM J. Sci. Comput. 33(1): 102-130 (2011)
2000 – 2009
- 2009
- [j1]Grigorios A. Pavliotis
Calculating effective diffusivities in the limit of vanishing molecular diffusion. J. Comput. Phys. 228(4): 1030-1055 (2009)
Coauthor Index
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