Markos A. Katsoulakis

67 papers A* 4Journal 62Unranked 1

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	Optimal Control for Transformer Architectures: Enhancing Generalization, Robustness and Efficiency. CoRR Kelvin Kan, Xingjian Li, Benjamin J. Zhang, Tuhin Sahai, Stanley J. Osher, Markos A. Katsoulakis
2025	J	`jnl`	Probabilistic operator learning: generative modeling and uncertainty quantification for foundation models of differential equations. CoRR Benjamin J. Zhang, Siting Liu, Stanley J. Osher, Markos A. Katsoulakis
2025	J	`jnl`	Stability of Transformers under Layer Normalization. CoRR Kelvin Kan, Xingjian Li, Benjamin J. Zhang, Tuhin Sahai, Stanley J. Osher, Krishna Kumar, Markos A. Katsoulakis
2025	J	`jnl`	Statistical Guarantees of Group-Invariant GANs. SIAM/ASA J. Uncertain. Quantification Ziyu Chen, Markos A. Katsoulakis, Luc Rey-Bellet, Wei Zhu
2024	J	`jnl`	Combining Wasserstein-1 and Wasserstein-2 proximals: robust manifold learning via well-posed generative flows. CoRR Hyemin Gu, Markos A. Katsoulakis, Luc Rey-Bellet, Benjamin J. Zhang
2024	J	`jnl`	Equivariant score-based generative models provably learn distributions with symmetries efficiently. CoRR Ziyu Chen, Markos A. Katsoulakis, Benjamin J. Zhang
2024	J	`jnl`	Learning heavy-tailed distributions with Wasserstein-proximal-regularized α-divergences. CoRR Ziyu Chen, Hyemin Gu, Markos A. Katsoulakis, Luc Rey-Bellet, Wei Zhu
2024	J	`jnl`	Lipschitz-Regularized Gradient Flows and Generative Particle Algorithms for High-Dimensional Scarce Data. SIAM J. Math. Data Sci. Hyemin Gu, Panagiota Birmpa, Yannis Pantazis, Luc Rey-Bellet, Markos A. Katsoulakis
2024	J	`jnl`	Nonlinear denoising score matching for enhanced learning of structured distributions. CoRR Jeremiah Birrell, Markos A. Katsoulakis, Luc Rey-Bellet, Benjamin J. Zhang, Wei Zhu
2024	A*	`conf`	Score-based generative models are provably robust: an uncertainty quantification perspective. NeurIPS Nikiforos Mimikos-Stamatopoulos, Benjamin J. Zhang, Markos A. Katsoulakis
2024	J	`jnl`	Score-based generative models are provably robust: an uncertainty quantification perspective. CoRR Nikiforos Mimikos-Stamatopoulos, Benjamin J. Zhang, Markos A. Katsoulakis
2024	J	`jnl`	Wasserstein proximal operators describe score-based generative models and resolve memorization. CoRR Benjamin J. Zhang, Siting Liu, Wuchen Li, Markos A. Katsoulakis, Stanley J. Osher
2023	J	`jnl`	A mean-field games laboratory for generative modeling. CoRR Benjamin J. Zhang, Markos A. Katsoulakis
2023	J	`jnl`	Cumulant GAN. IEEE Trans. Neural Networks Learn. Syst. Yannis Pantazis, Dipjyoti Paul, Michail Fasoulakis, Yannis Stylianou, Markos A. Katsoulakis
2023	A*	`conf`	Function-space regularized Rényi divergences. ICLR Jeremiah Birrell, Yannis Pantazis, Paul Dupuis, Luc Rey-Bellet, Markos A. Katsoulakis
2023	A*	`conf`	Sample Complexity of Probability Divergences under Group Symmetry. ICML Ziyu Chen, Markos A. Katsoulakis, Luc Rey-Bellet, Wei Zhu
2023	J	`jnl`	Statistical Guarantees of Group-Invariant GANs. CoRR Ziyu Chen, Markos A. Katsoulakis, Luc Rey-Bellet, Wei Zhu
2022	J	`jnl`	(f, Gamma)-Divergences: Interpolating between f-Divergences and Integral Probability Metrics. J. Mach. Learn. Res. Jeremiah Birrell, Paul Dupuis, Markos A. Katsoulakis, Yannis Pantazis, Luc Rey-Bellet
2022	J	`jnl`	Function-space regularized Rényi divergences. CoRR Jeremiah Birrell, Yannis Pantazis, Paul Dupuis, Markos A. Katsoulakis, Luc Rey-Bellet
2022	J	`jnl`	Lipschitz regularized gradient flows and latent generative particles. CoRR Hyemin Gu, Panagiota Birmpa, Yannis Pantazis, Luc Rey-Bellet, Markos A. Katsoulakis
2022	J	`jnl`	Model Uncertainty and Correctability for Directed Graphical Models. SIAM/ASA J. Uncertain. Quantification Panagiota Birmpa, Jinchao Feng, Markos A. Katsoulakis, Luc Rey-Bellet
2022	J	`jnl`	Optimizing Variational Representations of Divergences and Accelerating Their Statistical Estimation. IEEE Trans. Inf. Theory Jeremiah Birrell, Markos A. Katsoulakis, Yannis Pantazis
2022	A*	`conf`	Structure-preserving GANs. ICML Jeremiah Birrell, Markos A. Katsoulakis, Luc Rey-Bellet, Wei Zhu
2022	J	`jnl`	Structure-preserving GANs. CoRR Jeremiah Birrell, Markos A. Katsoulakis, Luc Rey-Bellet, Wei Zhu
2021	J	`jnl`	GINNs: Graph-Informed Neural Networks for multiscale physics. J. Comput. Phys. Eric Joseph Hall, Søren Taverniers, Markos A. Katsoulakis, Daniel M. Tartakovsky
2021	—	`conf`	Graph-Informed Neural Networks. AAAI Spring Symposium: MLPS Søren Taverniers, Eric Joseph Hall, Markos A. Katsoulakis, Daniel M. Tartakovsky
2021	J	`jnl`	Model Uncertainty and Correctability for Directed Graphical Models. CoRR Panagiota Birmpa, Jinchao Feng, Markos A. Katsoulakis, Luc Rey-Bellet
2021	J	`jnl`	Mutual information for explainable deep learning of multiscale systems. J. Comput. Phys. Søren Taverniers, Eric Joseph Hall, Markos A. Katsoulakis, Daniel M. Tartakovsky
2021	J	`jnl`	Uncertainty Quantification for Markov Random Fields. SIAM/ASA J. Uncertain. Quantification Panagiota Birmpa, Markos A. Katsoulakis
2021	J	`jnl`	Variational Representations and Neural Network Estimation of Rényi Divergences. SIAM J. Math. Data Sci. Jeremiah Birrell, Paul Dupuis, Markos A. Katsoulakis, Luc Rey-Bellet, Jie Wang
2020	J	`jnl`	(f, Γ)-Divergences: Interpolating between f-Divergences and Integral Probability Metrics. CoRR Jeremiah Birrell, Paul Dupuis, Markos A. Katsoulakis, Yannis Pantazis, Luc Rey-Bellet
2020	J	`jnl`	A Variational Formula for Rényi Divergences. CoRR Jeremiah Birrell, Paul Dupuis, Markos A. Katsoulakis, Luc Rey-Bellet, Jie Wang
2020	J	`jnl`	Cumulant GAN. CoRR Yannis Pantazis, Dipjyoti Paul, Michail Fasoulakis, Yannis Stylianou, Markos A. Katsoulakis
2020	J	`jnl`	Data-driven, variational model reduction of high-dimensional reaction networks. J. Comput. Phys. Markos A. Katsoulakis, Pedro Vilanova
2020	J	`jnl`	GINNs: Graph-Informed Neural Networks for Multiscale Physics. CoRR Eric Joseph Hall, Søren Taverniers, Markos A. Katsoulakis, Daniel M. Tartakovsky
2020	J	`jnl`	How Biased Is Your Model? Concentration Inequalities, Information and Model Bias. IEEE Trans. Inf. Theory Konstantinos Gourgoulias, Markos A. Katsoulakis, Luc Rey-Bellet, Jie Wang
2020	J	`jnl`	Mutual Information for Explainable Deep Learning of Multiscale Systems. CoRR Søren Taverniers, Eric Joseph Hall, Markos A. Katsoulakis, Daniel M. Tartakovsky
2020	J	`jnl`	Optimizing variational representations of divergences and accelerating their statistical estimation. CoRR Jeremiah Birrell, Markos A. Katsoulakis, Yannis Pantazis
2020	J	`jnl`	Uncertainty quantification for Markov Random Fields. CoRR Panagiota Birmpa, Markos A. Katsoulakis
2019	J	`jnl`	Causality and Bayesian Network PDEs for multiscale representations of porous media. J. Comput. Phys. Kimoon Um, Eric Joseph Hall, Markos A. Katsoulakis, Daniel M. Tartakovsky
2019	J	`jnl`	Robustness of Dynamical Quantities of Interest via Goal-Oriented Information Theory. CoRR Jeremiah Birrell, Markos A. Katsoulakis, Luc Rey-Bellet
2018	J	`jnl`	Computational Design of Complex Materials Using Information Theory: From Physics- to Data-driven Multi-scale Molecular Models. ERCIM News Vagelis Harmandaris, Evangelia Kalligiannaki, Markos A. Katsoulakis
2018	J	`jnl`	Robust Information Divergences for Model-Form Uncertainty Arising from Sparse Data in Random PDE. SIAM/ASA J. Uncertain. Quantification Eric Joseph Hall, Markos A. Katsoulakis
2017	J	`jnl`	How biased is your model? Concentration Inequalities, Information and Model Bias. CoRR Konstantinos Gourgoulias, Markos A. Katsoulakis, Luc Rey-Bellet, Jie Wang
2017	J	`jnl`	Information criteria for quantifying loss of reversibility in parallelized KMC. J. Comput. Phys. Konstantinos Gourgoulias, Markos A. Katsoulakis, Luc Rey-Bellet
2017	J	`jnl`	Scalable information inequalities for uncertainty quantification. J. Comput. Phys. Markos A. Katsoulakis, Luc Rey-Bellet, Jie Wang
2017	J	`jnl`	Special Issue: Predictive multiscale materials modeling. J. Comput. Phys. Markos A. Katsoulakis, Nicholas Zabaras
2016	J	`jnl`	Information Criteria for quantifying loss of reversibility in parallelized KMC. CoRR Konstantinos Gourgoulias, Markos A. Katsoulakis, Luc Rey-Bellet
2016	J	`jnl`	Information Metrics For Long-Time Errors in Splitting Schemes For Stochastic Dynamics and Parallel Kinetic Monte Carlo. SIAM J. Sci. Comput. Konstantinos Gourgoulias, Markos A. Katsoulakis, Luc Rey-Bellet
2016	J	`jnl`	Path-Space Information Bounds for Uncertainty Quantification and Sensitivity Analysis of Stochastic Dynamics. SIAM/ASA J. Uncertain. Quantification Paul Dupuis, Markos A. Katsoulakis, Yannis Pantazis, Petr Plechác
2016	J	`jnl`	Path-space variational inference for non-equilibrium coarse-grained systems. J. Comput. Phys. Vagelis Harmandaris, Evangelia Kalligiannaki, Markos A. Katsoulakis, Petr Plechác
2016	J	`jnl`	Scalable Information Inequalities for Uncertainty Quantification. CoRR Markos A. Katsoulakis, Luc Rey-Bellet, Jie Wang
2014	J	`jnl`	Coarse-graining schemes for stochastic lattice systems with short and long-range interactions. Math. Comput. Markos A. Katsoulakis, Petr Plechác, Luc Rey-Bellet, Dimitrios K. Tsagkarogiannis
2014	J	`jnl`	Parallelization, Processor Communication and Error Analysis in Lattice Kinetic Monte Carlo. SIAM J. Numer. Anal. Giorgos Arampatzis, Markos A. Katsoulakis, Petr Plechác
2014	J	`jnl`	Parametric Sensitivity Analysis for Stochastic Molecular Systems using Information Theoretic Metrics. CoRR Anastasios Tsourtis, Yannis Pantazis, Markos A. Katsoulakis, Vagelis Harmandaris
2014	J	`jnl`	Spatial Two-Level Interacting Particle Simulations and Information Theory-Based Error Quantification. SIAM J. Sci. Comput. Evangelia Kalligiannaki, Markos A. Katsoulakis, Petr Plechác
2013	J	`jnl`	Information-theoretic tools for parametrized coarse-graining of non-equilibrium extended systems CoRR Markos A. Katsoulakis, Petr Plechác
2013	J	`jnl`	Parametric Sensitivity Analysis for Biochemical Reaction Networks based on Pathwise Information Theory CoRR Yannis Pantazis, Markos A. Katsoulakis, Dionisios G. Vlachos
2013	J	`jnl`	Parametric sensitivity analysis for biochemical reaction networks based on pathwise information theory. BMC Bioinform. Yannis Pantazis, Markos A. Katsoulakis, Dionisios G. Vlachos
2012	J	`jnl`	Hierarchical fractional-step approximations and parallel kinetic Monte Carlo algorithms. J. Comput. Phys. Giorgos Arampatzis, Markos A. Katsoulakis, Petr Plechác, Michela Taufer, Lifan Xu
2012	J	`jnl`	Multilevel coarse graining and nano-pattern discovery in many particle stochastic systems. J. Comput. Phys. Evangelia Kalligiannaki, Markos A. Katsoulakis, Petr Plechác, Dionisios G. Vlachos
2011	J	`jnl`	Long-time integration methods for mesoscopic models of pattern-forming systems. J. Comput. Phys. Nasser Mohieddin Abukhdeir, Dionisios G. Vlachos, Markos A. Katsoulakis, Michael Plexousakis
2008	J	`jnl`	Multibody Interactions in Coarse-Graining Schemes for Extended Systems. SIAM J. Sci. Comput. Sasanka Are, Markos A. Katsoulakis, Petr Plechác, Luc Rey-Bellet
2008	J	`jnl`	Numerical and Statistical Methods for the Coarse-Graining of Many-Particle Stochastic Systems. J. Sci. Comput. Markos A. Katsoulakis, Petr Plechác, Luc Rey-Bellet
2006	J	`jnl`	Error Analysis of Coarse-Graining for Stochastic Lattice Dynamics. SIAM J. Numer. Anal. Markos A. Katsoulakis, Petr Plechác, Alexandros Sopasakis
2006	J	`jnl`	Stochastic Modeling and Simulation of Traffic Flow: Asymmetric Single Exclusion Process with Arrhenius look-ahead dynamics. SIAM J. Appl. Math. Alexandros Sopasakis, Markos A. Katsoulakis
2000	J	`jnl`	Hyperbolic Systems with Supercharacteristic Relaxations and Roll Waves. SIAM J. Appl. Math. Markos A. Katsoulakis, Shi Jin