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Markos A. Katsoulakis
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2020 – today
- 2024
- [i26]Benjamin J. Zhang, Siting Liu, Wuchen Li, Markos A. Katsoulakis, Stanley J. Osher:
Wasserstein proximal operators describe score-based generative models and resolve memorization. CoRR abs/2402.06162 (2024) - [i25]Ziyu Chen, Hyemin Gu, Markos A. Katsoulakis, Luc Rey-Bellet, Wei Zhu:
Learning heavy-tailed distributions with Wasserstein-proximal-regularized α-divergences. CoRR abs/2405.13962 (2024) - [i24]Jeremiah Birrell, Markos A. Katsoulakis, Luc Rey-Bellet, Benjamin J. Zhang, Wei Zhu:
Nonlinear denoising score matching for enhanced learning of structured distributions. CoRR abs/2405.15625 (2024) - [i23]Nikiforos Mimikos-Stamatopoulos, Benjamin J. Zhang, Markos A. Katsoulakis:
Score-based generative models are provably robust: an uncertainty quantification perspective. CoRR abs/2405.15754 (2024) - [i22]Hyemin Gu, Markos A. Katsoulakis, Luc Rey-Bellet, Benjamin J. Zhang:
Combining Wasserstein-1 and Wasserstein-2 proximals: robust manifold learning via well-posed generative flows. CoRR abs/2407.11901 (2024) - 2023
- [j30]Yannis Pantazis
, Dipjyoti Paul
Cumulant GAN. IEEE Trans. Neural Networks Learn. Syst. 34(11): 9439-9450 (2023) - [c4]Jeremiah Birrell
Function-space regularized Rényi divergences. ICLR 2023 - [c3]Ziyu Chen, Markos A. Katsoulakis, Luc Rey-Bellet, Wei Zhu:
Sample Complexity of Probability Divergences under Group Symmetry. ICML 2023: 4713-4734 - [i21]Benjamin J. Zhang, Markos A. Katsoulakis:
A mean-field games laboratory for generative modeling. CoRR abs/2304.13534 (2023) - [i20]Ziyu Chen, Markos A. Katsoulakis, Luc Rey-Bellet, Wei Zhu:
Statistical Guarantees of Group-Invariant GANs. CoRR abs/2305.13517 (2023) - 2022
- [j29]Jeremiah Birrell, Paul Dupuis, Markos A. Katsoulakis
(f, Gamma)-Divergences: Interpolating between f-Divergences and Integral Probability Metrics. J. Mach. Learn. Res. 23: 39:1-39:70 (2022) - [j28]Panagiota Birmpa, Jinchao Feng, Markos A. Katsoulakis
Model Uncertainty and Correctability for Directed Graphical Models. SIAM/ASA J. Uncertain. Quantification 10(4): 1461-1512 (2022) - [j27]Jeremiah Birrell
Optimizing Variational Representations of Divergences and Accelerating Their Statistical Estimation. IEEE Trans. Inf. Theory 68(7): 4553-4572 (2022) - [c2]Jeremiah Birrell, Markos A. Katsoulakis
Structure-preserving GANs. ICML 2022: 1982-2020 - [i19]Jeremiah Birrell, Markos A. Katsoulakis, Luc Rey-Bellet, Wei Zhu:
Structure-preserving GANs. CoRR abs/2202.01129 (2022) - [i18]Jeremiah Birrell, Yannis Pantazis, Paul Dupuis, Markos A. Katsoulakis, Luc Rey-Bellet:
Function-space regularized Rényi divergences. CoRR abs/2210.04974 (2022) - [i17]Hyemin Gu, Panagiota Birmpa, Yannis Pantazis, Luc Rey-Bellet, Markos A. Katsoulakis:
Lipschitz regularized gradient flows and latent generative particles. CoRR abs/2210.17230 (2022) - 2021
- [j26]Eric Joseph Hall
GINNs: Graph-Informed Neural Networks for multiscale physics. J. Comput. Phys. 433: 110192 (2021) - [j25]Søren Taverniers, Eric Joseph Hall
Mutual information for explainable deep learning of multiscale systems. J. Comput. Phys. 444: 110551 (2021) - [j24]Panagiota Birmpa, Markos A. Katsoulakis
Uncertainty Quantification for Markov Random Fields. SIAM/ASA J. Uncertain. Quantification 9(4): 1457-1498 (2021) - [j23]Jeremiah Birrell
Variational Representations and Neural Network Estimation of Rényi Divergences. SIAM J. Math. Data Sci. 3(4): 1093-1116 (2021) - [c1]Søren Taverniers, Eric Joseph Hall, Markos A. Katsoulakis, Daniel M. Tartakovsky:
Graph-Informed Neural Networks. AAAI Spring Symposium: MLPS 2021 - [i16]Panagiota Birmpa, Jinchao Feng, Markos A. Katsoulakis, Luc Rey-Bellet:
Model Uncertainty and Correctability for Directed Graphical Models. CoRR abs/2107.08179 (2021) - 2020
- [j22]Markos A. Katsoulakis
Data-driven, variational model reduction of high-dimensional reaction networks. J. Comput. Phys. 401 (2020) - [j21]Konstantinos Gourgoulias
How Biased Is Your Model? Concentration Inequalities, Information and Model Bias. IEEE Trans. Inf. Theory 66(5): 3079-3097 (2020) - [i15]Yannis Pantazis, Dipjyoti Paul, Michail Fasoulakis, Yannis Stylianou, Markos A. Katsoulakis:
Cumulant GAN. CoRR abs/2006.06625 (2020) - [i14]Jeremiah Birrell, Markos A. Katsoulakis, Yannis Pantazis:
Optimizing variational representations of divergences and accelerating their statistical estimation. CoRR abs/2006.08781 (2020) - [i13]Eric Joseph Hall
GINNs: Graph-Informed Neural Networks for Multiscale Physics. CoRR abs/2006.14807 (2020) - [i12]Jeremiah Birrell, Paul Dupuis, Markos A. Katsoulakis, Luc Rey-Bellet, Jie Wang:
A Variational Formula for Rényi Divergences. CoRR abs/2007.03814 (2020) - [i11]Panagiota Birmpa, Markos A. Katsoulakis:
Uncertainty quantification for Markov Random Fields. CoRR abs/2009.00038 (2020) - [i10]Søren Taverniers, Eric Joseph Hall
Mutual Information for Explainable Deep Learning of Multiscale Systems. CoRR abs/2009.04570 (2020) - [i9]Jeremiah Birrell, Paul Dupuis, Markos A. Katsoulakis, Yannis Pantazis, Luc Rey-Bellet:
(f, Γ)-Divergences: Interpolating between f-Divergences and Integral Probability Metrics. CoRR abs/2011.05953 (2020)
2010 – 2019
- 2019
- [j20]Kimoon Um
Causality and Bayesian Network PDEs for multiscale representations of porous media. J. Comput. Phys. 394: 658-678 (2019) - [i8]Jeremiah Birrell, Markos A. Katsoulakis, Luc Rey-Bellet:
Robustness of Dynamical Quantities of Interest via Goal-Oriented Information Theory. CoRR abs/1906.09282 (2019) - 2018
- [j19]Eric Joseph Hall
Robust Information Divergences for Model-Form Uncertainty Arising from Sparse Data in Random PDE. SIAM/ASA J. Uncertain. Quantification 6(4): 1364-1394 (2018) - [i7]Vagelis Harmandaris, Evangelia Kalligiannaki, Markos A. Katsoulakis:
Computational Design of Complex Materials Using Information Theory: From Physics- to Data-driven Multi-scale Molecular Models. ERCIM News 2018(115) (2018) - 2017
- [j18]Konstantinos Gourgoulias, Markos A. Katsoulakis
Information criteria for quantifying loss of reversibility in parallelized KMC. J. Comput. Phys. 328: 438-454 (2017) - [j17]Markos A. Katsoulakis
Scalable information inequalities for uncertainty quantification. J. Comput. Phys. 336: 513-545 (2017) - [j16]Markos A. Katsoulakis
Special Issue: Predictive multiscale materials modeling. J. Comput. Phys. 338: 18-20 (2017) - [i6]Konstantinos Gourgoulias, Markos A. Katsoulakis, Luc Rey-Bellet, Jie Wang:
How biased is your model? Concentration Inequalities, Information and Model Bias. CoRR abs/1706.10260 (2017) - 2016
- [j15]Vagelis Harmandaris
Path-space variational inference for non-equilibrium coarse-grained systems. J. Comput. Phys. 314: 355-383 (2016) - [j14]Paul Dupuis, Markos A. Katsoulakis
Path-Space Information Bounds for Uncertainty Quantification and Sensitivity Analysis of Stochastic Dynamics. SIAM/ASA J. Uncertain. Quantification 4(1): 80-111 (2016) - [j13]Konstantinos Gourgoulias, Markos A. Katsoulakis
Information Metrics For Long-Time Errors in Splitting Schemes For Stochastic Dynamics and Parallel Kinetic Monte Carlo. SIAM J. Sci. Comput. 38(6) (2016) - [i5]Konstantinos Gourgoulias, Markos A. Katsoulakis, Luc Rey-Bellet:
Information Criteria for quantifying loss of reversibility in parallelized KMC. CoRR abs/1605.02375 (2016) - [i4]Markos A. Katsoulakis, Luc Rey-Bellet, Jie Wang:
Scalable Information Inequalities for Uncertainty Quantification. CoRR abs/1605.04184 (2016) - 2014
- [j12]Markos A. Katsoulakis
Coarse-graining schemes for stochastic lattice systems with short and long-range interactions. Math. Comput. 83(288): 1757-1793 (2014) - [j11]Giorgos Arampatzis, Markos A. Katsoulakis
Parallelization, Processor Communication and Error Analysis in Lattice Kinetic Monte Carlo. SIAM J. Numer. Anal. 52(3): 1156-1182 (2014) - [j10]Evangelia Kalligiannaki
Spatial Two-Level Interacting Particle Simulations and Information Theory-Based Error Quantification. SIAM J. Sci. Comput. 36(2) (2014) - [i3]Anastasios Tsourtis, Yannis Pantazis, Markos A. Katsoulakis, Vagelis Harmandaris:
Parametric Sensitivity Analysis for Stochastic Molecular Systems using Information Theoretic Metrics. CoRR abs/1412.6482 (2014) - 2013
- [j9]Yannis Pantazis, Markos A. Katsoulakis
Parametric sensitivity analysis for biochemical reaction networks based on pathwise information theory. BMC Bioinform. 14: 311 (2013) - [i2]Yannis Pantazis, Markos A. Katsoulakis, Dionisios G. Vlachos:
Parametric Sensitivity Analysis for Biochemical Reaction Networks based on Pathwise Information Theory. CoRR abs/1304.3962 (2013) - [i1]Markos A. Katsoulakis, Petr Plechác:
Information-theoretic tools for parametrized coarse-graining of non-equilibrium extended systems. CoRR abs/1304.7700 (2013) - 2012
- [j8]Evangelia Kalligiannaki
Multilevel coarse graining and nano-pattern discovery in many particle stochastic systems. J. Comput. Phys. 231(6): 2599-2620 (2012) - [j7]Giorgos Arampatzis, Markos A. Katsoulakis
Hierarchical fractional-step approximations and parallel kinetic Monte Carlo algorithms. J. Comput. Phys. 231(23): 7795-7814 (2012) - 2011
- [j6]Nasser Mohieddin Abukhdeir, Dionisios G. Vlachos
Long-time integration methods for mesoscopic models of pattern-forming systems. J. Comput. Phys. 230(14): 5704-5715 (2011)
2000 – 2009
- 2008
- [j5]Markos A. Katsoulakis
Numerical and Statistical Methods for the Coarse-Graining of Many-Particle Stochastic Systems. J. Sci. Comput. 37(1): 43-71 (2008) - [j4]Sasanka Are, Markos A. Katsoulakis
Multibody Interactions in Coarse-Graining Schemes for Extended Systems. SIAM J. Sci. Comput. 31(2): 987-1015 (2008) - 2006
- [j3]Alexandros Sopasakis
Stochastic Modeling and Simulation of Traffic Flow: Asymmetric Single Exclusion Process with Arrhenius look-ahead dynamics. SIAM J. Appl. Math. 66(3): 921-944 (2006) - [j2]Markos A. Katsoulakis
Error Analysis of Coarse-Graining for Stochastic Lattice Dynamics. SIAM J. Numer. Anal. 44(6): 2270-2296 (2006) - 2000
- [j1]Markos A. Katsoulakis
Hyperbolic Systems with Supercharacteristic Relaxations and Roll Waves. SIAM J. Appl. Math. 61(1): 273-292 (2000)
Coauthor Index
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