Carlon, A. G., de Carvalho Dantas Maia, C. D., Lopez, R. H., Torii, A. J., & Miguel, L. F. F. (2023). A polynomial chaos efficient global optimization approach for Bayesian optimal experimental design. Probabilistic Engineering Mechanics, 72, 103454. https://doi.org/10.1016/j.probengmech.2023.103454
Chaabane, K. B., Kebaier, A., Scavino, M., & Tempone, R. (2023). Data-driven uncertainty quantification for constrained stochastic differential equations and application to solar photovoltaic power forecast data (Version 1). arXiv. https://doi.org/10.48550/ARXIV.2302.13133
Hammouda, C. B., Rezvanova, E., von Schwerin, E., & Tempone, R. (2023). Lagrangian Relaxation for Continuous-Time Optimal Control of Coupled Hydrothermal Power Systems Including Storage Capacity and a Cascade of Hydropower Systems with Time Delays (Version 1). arXiv. https://doi.org/10.48550/ARXIV.2311.00794


Amar, E. B., Rached, N. B., Haji-Ali, A.-L., & Tempone, R. (2022). Efficient Importance Sampling Algorithm Applied to the Performance Analysis of Wireless Communication Systems Estimation. arXiv preprint, ArXiv: 2201.01240
Bayer, C., Hammouda, C. B., Papapantoleon, A., Samet, M., & Tempone, R. (2022). Optimal Damping with Hierarchical Adaptive Quadrature for Efficient Fourier Pricing of Multi-Asset Options in Lévy Models. arXiv preprint, ArXiv: 2203.08196
Rached, N. B., Haji-Ali, A.-L., Pillai, S. M. S., & Tempone, R. (2022). Multilevel Importance Sampling for McKean-Vlasov Stochastic Differential Equation. arXiv. https://doi.org/10.48550/ARXIV.2208.03225
Bayer, C., Hammouda, C. B., Papapantoleon, A., Samet, M., & Tempone, R. (2022). Optimal Damping with Hierarchical Adaptive Quadrature for Efficient Fourier Pricing of Multi-Asset Options in Lévy Models (Version 2). arXiv. https://doi.org/10.48550/ARXIV.2203.08196
Beck, J., Liu, Y., von Schwerin, E., & Tempone, R. (2022). Goal-Oriented Adaptive Finite Element Multilevel Monte Carlo with Convergence Rates (Version 2). arXiv. https://doi.org/10.48550/ARXIV.2206.10314
Carlon, A., Espath, L., & Tempone, R. (2022). Approximating Hessian matrices using Bayesian inference: a new approach for quasi-Newton methods in stochastic optimization. arXiv. https://doi.org/10.48550/ARXIV.2208.00441
Amar, E. B., Rached, N. B., Haji-Ali, A.-L., & Tempone, R. (2022). State-dependent Importance Sampling for Estimating Expectations of Functionals of Sums of Independent Random Variables. arXiv. https://doi.org/10.48550/ARXIV.2201.01340
Rached, N. B., Haji-Ali, A.-L., Pillai, S. M. S., & Tempone, R. (2022). Single Level Importance Sampling for McKean-Vlasov Stochastic Differential Equation (Version 4). arXiv. https://doi.org/10.48550/ARXIV.2207.06926
Cramer, E., Rauh, F., Mitsos, A., Tempone, R., & Dahmen, M. (2022). Nonlinear Isometric Manifold Learning for Injective Normalizing Flows (Version 1). arXiv. https://doi.org/10.48550/ARXIV.2203.03934


Hammouda, C. B., Rached, N. B., Tempone, R., & Wiechert, S. (2021). Learning-Based Importance Sampling via Stochastic Optimal Control for Stochastic Reaction Networks. arXiv. https://doi.org/10.48550/ARXIV.2110.14335
Bartuska, A., Espath, L., & Tempone, R. (2021). Small-noise approximation for Bayesian optimal experimental design with nuisance uncertainty. ArXiv. https://doi.org/10.48550/ARXIV.2112.06794
Haji-Ali, A.-L., Hoel, H., & Tempone, R. (2021). A simple approach to proving the existence, uniqueness, and strong and weak convergence rates for a broad class of McKean--Vlasov equations. arXiv. https://doi.org/10.48550/ARXIV.2101.00886
Espath, L., Krumscheid, S., Tempone, R., & Vilanova, P. (2021). On the equivalence of different adaptive batch size selection strategies for stochastic gradient descent methods. arXiv. https://doi.org/10.48550/ARXIV.2109.10933
Hammouda, C. B., Rached, N. B., Tempone, R., & Wiechert, S. (2021). Learning-Based Importance Sampling via Stochastic Optimal Control for Stochastic Reaction Networks (Version 4). arXiv. https://doi.org/10.48550/ARXIV.2110.14335
Hoang, T.-V., Krumscheid, S., Matthies, H. G., & Tempone, R. (2021). Machine learning-based conditional mean filter: a generalization of the ensemble Kalman filter for nonlinear data assimilation (Version 2). arXiv. https://doi.org/10.48550/ARXIV.2106.07908
Bayer, C., Hammouda, C. B., & Tempone, R. (2021). Numerical Smoothing with Hierarchical Adaptive Sparse Grids and Quasi-Monte Carlo Methods for Efficient Option Pricing. arXiv. https://doi.org/10.48550/ARXIV.2111.01874
Madrigal-Cianci, J. P., Nobile, F., & Tempone, R. (2021). Analysis of a class of Multi-Level Markov Chain Monte Carlo algorithms based on Independent Metropolis-Hastings (Version 1). arXiv. https://doi.org/10.48550/ARXIV.2105.02035
Hoel, H., Shaimerdenova, G., & Tempone, R. (2021). Multi-index ensemble Kalman filtering. ArXiv. https://doi.org/10.48550/ARXIV.2104.07263


Caballero, R., Kebaier, A., Scavino, M., & Tempone, R. (2020). Quantifying Uncertainty with a Derivative Tracking SDE Model and Application to Wind Power Forecast Data (Version 2). arXiv. https://doi.org/10.48550/ARXIV.2006.15907
Kammonen, A., Kiessling, J., Plecháč, P., Sandberg, M., Szepessy, A., & Tempone, R. (2020). Smaller generalization error derived for a deep residual neural network compared to shallow networks (Version 2). arXiv. https://doi.org/10.48550/ARXIV.2010.01887
Carlon, A., Espath, L., Lopez, R., & Tempone, R. (2020). Multi-Iteration Stochastic Optimizers. arXiv. https://doi.org/10.48550/ARXIV.2011.01718
Bayer, C., Hammouda, C. B., & Tempone, R. (2020). Multilevel Monte Carlo combined with numerical smoothing for robust and efficient option pricing and density estimation. arXiv. https://doi.org/10.48550/ARXIV.2003.05708
Bayer, C., Hall, E. J., & Tempone, R. (2020). Weak error rates for option pricing under linear rough volatility. arXiv. https://doi.org/10.48550/ARXIV.2009.01219


Rached, N. B., Kammoun, A., Alouini, M.-S., & Tempone, R. (2019). An Accurate Sample Rejection Estimator for the Estimation of Outage Probability of EGC Receivers. arXiv. https://doi.org/10.48550/ARXIV.1903.05481
Issaid, C. B., Alouini, M.-S., & Tempone, R. (2019). Eficient Monte Carlo Simulation of the Left Tail of Positive Gaussian Quadratic Forms. arXiv. https://doi.org/10.48550/ARXIV.1901.09174
Hammouda, C. B., Rached, N. B., & Tempone, R. (2019). Importance sampling for a robust and efficient multilevel Monte Carlo estimator for stochastic reaction networks. ArXiv. https://doi.org/10.48550/ARXIV.1911.06286


Carlon, A. G., Dia, B. M., Espath, L. F. R., Lopez, R. H., & Tempone, R. (2018). Nesterov-aided Stochastic Gradient Methods using Laplace Approximation for Bayesian Design Optimization. arXiv. https://doi.org/10.48550/ARXIV.1807.00653
Beck, J., Tamellini, L., & Tempone, R. (2018). IGA-based Multi-Index Stochastic Collocation for random PDEs on arbitrary domains. ArXiv. https://doi.org/10.48550/ARXIV.1810.01661
Bayer, C., Hammouda, C. B., & Tempone, R. (2018). Hierarchical adaptive sparse grids and quasi Monte Carlo for option pricing under the rough Bergomi model. ArXiv. https://doi.org/10.48550/ARXIV.1812.08533
Bayer, C., Tempone, R., & Wolfers, S. (2018). Pricing American Options by Exercise Rate Optimization. arXiv. https://doi.org/10.48550/ARXIV.1809.07300
Beck, J., Dia, B. M., Espath, L. F. R., & Tempone, R. (2018). Multilevel Double Loop Monte Carlo and Stochastic Collocation Methods with Importance Sampling for Bayesian Optimal Experimental Design. arXiv. https://doi.org/10.48550/ARXIV.1811.11469
Litvinenko, A., Yucel, A. C., Bagci, H., Oppelstrup, J., Michielssen, E., & Tempone, R. (2018). Computation of Electromagnetic Fields Scattered From Objects With Uncertain Shapes Using Multilevel Monte Carlo Method. arXiv. https://doi.org/10.48550/ARXIV.1809.00362


Litvinenko, A., Keyes, D., Khoromskaia, V., Khoromskij, B. N., & Matthies, H. G. (2017). Tucker Tensor analysis of Matern functions in spatial statistics. arXiv. https://doi.org/10.48550/ARXIV.1711.06874
Haji-Ali, A.-L., Nobile, F., Tempone, R., & Wolfers, S. (2017). Multilevel weighted least squares polynomial approximation. arXiv. https://doi.org/10.48550/ARXIV.1707.00026
Bayer, C., Häppölä, J., & Tempone, R. (2017). Implied Stopping Rules for American Basket Options from Markovian Projection. arXiv. https://doi.org/10.48550/ARXIV.1705.00558
Litvinenko, A. (2017). HLIBCov: Parallel Hierarchical Matrix Approximation of Large Covariance Matrices and Likelihoods with Applications in Parameter Identification. arXiv. https://doi.org/10.48550/ARXIV.1709.08625
Alouini, M.-S., Rached, N. B., Kammoun, A., & Tempone, R. (2017). On the Efficient Simulation of the Left-Tail of the Sum of Correlated Log-normal Variates. arXiv. https://doi.org/10.48550/ARXIV.1705.07635
Chernov, A., Hoel, H., Law, K. J. H., Nobile, F., & Tempone, R. (2017). Multilevel ensemble Kalman filtering for spatio-temporal processes. arXiv. https://doi.org/10.48550/ARXIV.1710.07282
Tempone, R., & Wolfers, S. (2017). Smolyak's algorithm: A powerful black box for the acceleration of scientific computations. arXiv. https://doi.org/10.48550/ARXIV.1703.08872


Bayer, C., Siebenmorgen, M., & Tempone, R. (2016). Smoothing the payoff for efficient computation of Basket option prices. arXiv. https://doi.org/10.48550/ARXIV.1607.05572
Garra, R., Orsingher, E., & Scavino, M. (2016). Some probabilistic properties of fractional point processes. arXiv. https://doi.org/10.48550/ARXIV.1604.05235
Nobile, F., Tempone, R., & Wolfers, S. (2016). Sparse approximation of multilinear problems with applications to kernel-based methods in UQ. arXiv. https://doi.org/10.48550/ARXIV.1609.00246
Matthies, H. G., Zander, E., Rosic, B., & Litvinenko, A. (2016). Parameter Estimation via Conditional Expectation --- A Bayesian Inversion. arXiv. https://doi.org/10.48550/ARXIV.1606.09440
Matthies, H. G., Litvinenko, A., Rosic, B. V., & Zander, E. (2016). Bayesian Parameter Estimation via Filtering and Functional Approximations. arXiv. https://doi.org/10.48550/ARXIV.1611.09293


Hall, E. J., Hoel, H., Sandberg, M., Szepessy, A., & Tempone, R. (2015). Computable error estimates for finite element approximations of elliptic partial differential equations with rough stochastic data. ArXiv. https://doi.org/10.48550/ARXIV.1510.02708
Beskos, A., Jasra, A., Law, K., Tempone, R., & Zhou, Y. (2015). Multilevel Sequential Monte Carlo Samplers. ArXiv. https://doi.org/10.48550/ARXIV.1503.07259
Chen, Y., Keyes, D., Law, K. J. H., & Ltaief, H. (2015). Accelerated dimension-independent adaptive Metropolis. ArXiv. https://doi.org/10.48550/ARXIV.1506.05741
Aseeri, S., Batrašev, O., Icardi, M., Leu, B., Liu, A., Li, N., Muite, B. K., Müller, E., Palen, B., Quell, M., Servat, H., Sheth, P., Speck, R., Van Moer, M., & Vienne, J. (2015). Solving the Klein-Gordon equation using Fourier spectral methods: A benchmark test for computer performance. arXiv. https://doi.org/10.48550/ARXIV.1501.04552
Liu, D., Litvinenko, A., Schillings, C., & Schulz, V. (2015). Quantification of airfoil geometry-induced aerodynamic uncertainties - comparison of approaches. arXiv. https://doi.org/10.48550/ARXIV.1505.05731


Rached, N. B., Benkhelifa, F., Kammoun, A., Alouini, M.-S., & Tempone, R. (2014). A Fast Simulation Method for the Sum of Subexponential Distributions. arXiv. https://doi.org/10.48550/ARXIV.1406.4689
Law, K. J. H., Sanz-Alonso, D., Shukla, A., & Stuart, A. M. (2014). Controlling Unpredictability with Observations in the Partially Observed Lorenz '96 Model. ArXiv. https://doi.org/10.48550/ARXIV.1411.3113
Law, K. J. H., Tembine, H., & Tempone, R. (2014). Deterministic Mean-field Ensemble Kalman Filtering. ArXiv. https://doi.org/10.48550/ARXIV.1409.0628
Hoel, H., Häppölä, J., & Tempone, R. (2014). Construction of a Mean Square Error Adaptive Euler--Maruyama Method with Applications in Multilevel Monte Carlo. arXiv. https://doi.org/10.48550/ARXIV.1411.5515
Cui, T., Law, K. J. H., & Marzouk, Y. M. (2014). Dimension-independent likelihood-informed MCMC. ArXiv. https://doi.org/10.48550/ARXIV.1411.3688


Bayer, C., Hoel, H., Kadir, A., Plechac, P., Sandberg, M., & Szepessy, A. (2013). Computational error estimates for Born-Oppenheimer molecular dynamics with nearly crossing potential surfaces. arXiv. https://doi.org/10.48550/ARXIV.1305.3330


Tembine, H., Tempone, R., & Vilanova, P. (2012). Mean-Field Learning: a Survey. arXiv. https://doi.org/10.48550/ARXIV.1210.4657


Bayer, C., Hoel, H., Plecháč, P., Szepessy, A., & Tempone, R. (2011). How accurate is molecular dynamics?. arXiv. https://doi.org/10.48550/ARXIV.1104.0953