Research published papers

• Policy gradient learning methods for stochastic control with exit time and applications to share repurchase pricing 

(with M. Hamdouche and P. Henry-Labordère)

Applied Mathematical Finance, 2023, 29(6), 439-456,   [ResearchGate], [SSRN]

• Mean-field neural networks: learning mappings on Wasserstein space, (with X. Warin)

Neural Networks, 2023, vol. 168, 380-393, https://doi.org/10.1016/j.neunet.2023.09.015, [ResearchGate], [arXiv:2210.15179]

• Quantitative propagation of chaos for mean field Markov decision process with common noise (with M. Motte)

Electronic Journal of Probability, 2023, Vol 28, 1-24.  [ResearchGate], [arXiv:2207.12738],

• Differential learning methods for solving fully nonlinear PDEs (with W. Lefebvre, G. Loeper)

Digital Finance, 2023, vol 5, 183-229,   [Researchgate],[arXiv:2205:09815]

• A level-set approach to the control of state-constrained McKean-Vlasov equations: application to renewable energy storage and portfolio selection 

(with M. Germain, X. Warin)

Numerical Algebra, Control and Optimization, special volume in honor of Jin Ma, 2023, vol 13(3&4): 555-582, [arXiv:2112.11059]  

• Neural networks-based algorithms for stochastic control and PDEs in finance (with M. Germain, X. Warin)

Machine Learning and Data Sciences for Financial Markets: a guide to contemporary practices, Cambridge University Press,  2023,  Editors: Agostino Capponi and Charles-Albert Lehalle, [arXiv:2101.08068]

• Optimal control of path-dependent McKean-Vlasov SDEs in infinite dimension (with A. Cosso, F. Gozzi, I. Kharroubi, M. Rosestolato)

Annals of Applied Probability, 2023, 33(4), 2863-2918, DOI: 10.1214/22-AAP1880, [Researchgate], [arXiv:2012.14772]

• Itô’s formula for flow of measures on semimartingales (with X. Guo, X. Wei)

Stochastic Processes and their Applications, 2023, Vol. 159, pp 350-390, https://doi.org/10.1016/j.spa.2023.02.004, [arXiv:2010.05288]

• Rate of convergence for particles approximation of PDEs in Wasserstein space (with M. Germain, X. Warin)

Journal of Applied Probability, 2022, vol 59(4), 992-1008,

• Discrete-time portfolio optimization under maximum drawdown constraint with partial information and deep learning resolution 

(with C. De Franco, J. Nicolle)

Stochastic Analysis, Filtering, and Stochastic Optimizations: A Commemorative Volume to Honor Mark H. A. Davis's Contributions,  2022, eds. G. Yin, T. Zariphopoulou,

• DeepSets and their derivative networks for solving symmetric PDEs (with M. Germain, M. Laurière, X. Warin)

Journal of Scientific Computing, 2022, Vol 91, article 63, https://doi.org/10.1007/s10915-022-01796-w  

• Mean-field Markov decision process with common noise and open-loop controls (with M. Motte)

Annals of Applied Probability, 2022, Vol 32(2), pp 1421-1458 

• Deep neural networks algorithms for stochastic control problems on finite horizon: numerical applications 

(with A. Bachouch, C. Huré, N. Langrené)

Methodology and Computing in Applied Probability, 2022, Vol 24, pp 143-178.

• Approximation error analysis of some deep backward schemes for nonlinear PDEs (with M. Germain, X. Warin)

SIAM Journal of Scientific Computing, 2022, Vol 44(1), A28-A56 

• Equilibrium price in intraday electricity markets (with R. Aid, A. Cosso)

Mathematical Finance, 2022, Vol 32(2), pp 517-554

• Optimal Consumption with Reference to Past Spending Maximum (with S. Deng, X. Li, X. Yu)

Finance and Stochastics, 2022, Vol 26, pp 217-266

• Portfolio diversification and model uncertainty: a robust dynamic mean-variance approach (with X. Wei, C. Zhou)

Mathematical Finance,  2022, vol 32(1), 349-404, https://doi.org/10.1111/mafi.12320,  

• Markowitz portfolio selection for multivariate affine and quadratic Volterra models (with E. Abi Jaber, E. Miller)

SIAM Journal of Financial Mathematics, 2021, Vol. 12, No. 1, pp. 369-409

• Linear--Quadratic control for a class of stochastic Volterra equations: solvability and approximation (with E. Abi Jaber, E. Miller)

Annals of Applied Probability, 2021, Vol 31(5), pp 2244-2274

• Integral operator Riccati equations arising in stochastic Volterra control problems (with E. Abi Jaber, E. Miller)

SIAM Journal on Control and Optimization, 2021, Vol. 59, No. 2, pp. 1581-1603.

• Neural networks-based backward scheme for fully nonlinear PDEs (with M. Germain, X. Warin)

SN Partial Differential Equations and Applications, 2021, Vol 2(1), article 16. 

•  Deep neural networks algorithms for stochastic control problems on finite horizon: convergence analysis 

(with C. Huré, A. Bachouch, N. Langrené)

SIAM Journal on Numerical Analysis,  2021, 59(1), 525-557. 

• Deep backward schemes for high-dimensional nonlinear PDEs 

(formerly entitled: Some machine learning schemes for high-dimensional nonlinear PDEs), (with C. Huré, X. Warin)

Mathematics of Computation, 2020, vol 89(324), pp. 1547-1580

• Mean-variance portfolio selection with tracking error penalization (with W. Lefebvre, G. Loeper)

Mathematics, 2020, vol 8(11), Special Issue Stochastic Optimization Methods in Economics, Finance and Insurance,  https://doi.org/10.3390/math8111915

• A McKean-Vlasov approach to distributed electricity generation development (with R. Aid, M. Basei)

Mathematical Methods of Operations Research, 2020, vol 91, pp. 269–310,