PUBLICATIONS


PUBLISHED AND ACCEPTED PAPERS

2022

2021

2020

2019

WORKSHOP PRESENTATION

2022

  • M. Chataigner, A. Cousin, S. Crépey, M. Dixon, and D. Gueye, Beyond surrogate modeling: Learning the
    local volatility via shape constraints, SIAM Conference on Financial Mathematics and Engineering, Jun. 2022..

2021

  • C. Albanese, S. Crépey, and S. Iabichino, Reverse stress testing, Journée de rentrée du LPSM, Université de
    Paris, Sep. 2021.
  • C. Albanese, S. Crépey, and S. Iabichino, Reverse stress testing, Reverse Stress testing 3.0 - Third Annual Masterclass, Nov. 2021.
  • M. Allouche, S. Girard, and E. Gobet, Generative model for fbm with deep ReLU neural networks, Bernoulli-IMS 10th World Congress in Probability and Statistics, Jul. 2021.
  • M. Allouche, S. Girard, and E. Gobet, On the approximation of extreme quantiles with ReLU neural networks,
    12th International Conference on Extreme Value Analysis, Jun. 2021.
  • M. Bousebata, G. Enjolras, and S. Girard, Extreme partial least-squares regression, 12th International Conference on Extreme Value Analysis, Jun. 2021.
  • J. El-Methni and S. Girard, A bias-reduced version of the Weissman extreme quantile estimator, 12th International Conference on Extreme Value Analysis, Jun. 2021.
  • A. Usseglio-Carleve, S. Girard, and G. Stupfler, Extreme expectile regression: theory and applications, 12th
    International Conference on Extreme Value Analysis, Jun. 2021.

2020

  • Aubin-Frankowski, P.-C., Szabo, Z. (2020). Hard shape constrained kernel machines. Advances in Neural Information Processing Systems (neurIPS-2020), Dec 2020, Vancouver (Virtual), Canada.
  • C. Albanese, S. Crépey, and S. Iabichino, Reverse stress testing, StressTest-2021 : International Workshop on Stress Test and Risk Management, Paris, Dec. 20

2019


PREPRINTS

2022

2021

  • Aubin-Frankowski, P.-C., and Szabo Z., (2021). Handling hard affine SDP shape contraints in RKHSs, Technical reports.
  • Bourgey, F., De Marco, S. (2021). Multilevel Monte Carlo simulation for VIX options in the rough Bergomi model.
  • Bourgey, F., De Marco, S., Gobet, E. (2021). Weak approximations and VIX options prices expansions in rough Bergomi models.
  • Bourgey, F., Gobet, E., Rey, C. (2021). A comparative study of polynomial-type chaos expansions for indicator functions.
  • Bourgey, F., Gobet, E., Rey, C. (2021). Polynomial chaos expansion and meta-model of large sums of dependent random variables.
  • Girard, S., Stupfler, G., Usseglio-Carleve, A., (2021). Extreme L^p - quantile kernel regression.
  • Lambert, A., Parekh, S., Szabo, Z., and Alché-Buc, F., (2021). Emotion transfert using vector-valued infinite task learning. Technical reports, submitted to a special track of Machine Learning.

2020

2019

  • Barrera, D., Gobet, E. (2019). Generalization bounds for nonparametric regression with B-mixing samples.