- Abbas‐Turki, L. A., Vialle, S., Lapeyre, B., & Mercier, P. (2014). Pricing derivatives on graphics processing units using Monte Carlo simulation. Concurrency and Computation: Practice and Experience, 26(9), 1679–1697.
Paper not yet in RePEc: Add citation now
Abbas‐Turki, L., Diallo, B., & Crépey, S. (2018). XVA principles, nested Monte Carlo strategies, and GPU optimizations. International Journal of Theoretical and Applied Finance, 21, 1850030.
Albanese, C., Crépey, S., Hoskinson, R., & Saadeddine, B. (2021). XVA analysis from the balance sheet. Quantitative Finance, 21(1), 99–123.
- Bengio, Y., Courville, A., & Goodfellow, I. (2016). Deep learning. MIT Press.
Paper not yet in RePEc: Add citation now
- Bergstra, J., & Bengio, Y. (2012). Random search for hyper‐parameter optimization. Journal of Machine Learning Research, 13, 281–305.
Paper not yet in RePEc: Add citation now
- Bozinovski, S. (2020). Reminder of the first paper on transfer learning in neural networks, 1976. Informatica, 44(3), 291‐302.
Paper not yet in RePEc: Add citation now
Carmona, R., & Crépey, S. (2010). Particle methods for the estimation of credit portfolio loss distributions. International Journal of Theoretical and Applied Finance, 13(04), 577–602.
- Cesari, J., Aquilina, J., & Charpillon, N. (2010). Modelling, Pricing, and Hedging Counterparty Credit Exposure. Springer.
Paper not yet in RePEc: Add citation now
- Chizat, L., & Bach, F. (2018). On the global convergence of gradient descent for over‐parameterized models using optimal transport. In Proceedings of the Advances in Neural Information Processing Systems, 31.
Paper not yet in RePEc: Add citation now
- Choromanska, A., Henaff, M., Mathieu, M., Arous, G. B., & LeCun, Y. (2015). The loss surfaces of multilayer networks. In Artificial intelligence and statistics, (pp. 192–204). PMLR.
Paper not yet in RePEc: Add citation now
- Crépey, S. (2022). Positive XVAs. Frontiers of Mathematical Finance, 1(3), 425–465.
Paper not yet in RePEc: Add citation now
Crépey, S., & Song, S. (2015). BSDEs of counterparty risk. Stochastic Processes and their Applications, 125(8), 3023–3052.
- Cybenko, G. (1989). Approximation by superpositions of a sigmoidal function. Mathematics of Control, Signals and Systems, 2(4), 303–314.
Paper not yet in RePEc: Add citation now
- Du, S., Lee, J., Li, H., Wang, L., & Zhai, X. (2019). Gradient descent finds global minima of deep neural networks. In International Conference on Machine Learning, (pp. 1675–1685). Proceedings of Machine Learning Research.
Paper not yet in RePEc: Add citation now
- Ee, W., Han, J., & Jentzen, A. (2017). Deep learning‐based numerical methods for high‐dimensional parabolic partial differential equations and backward stochastic differential equations. Communications in Mathematics and Statistics, 5(4), 370–398.
Paper not yet in RePEc: Add citation now
- Glasserman, P. (2004). Monte Carlo methods in financial engineering. Applications of mathematics, stochastic modelling and applied probability. Springer.
Paper not yet in RePEc: Add citation now
Gnoatto, A., Reisinger, C., & Picarelli, A. (2020). Deep xVA solver—a neural network based counterparty credit risk management framework. Available at SSRN 3594076.
Gordy, M. B., & Juneja, S. (2010). Nested simulation in portfolio risk measurement. Management Science, 56(10), 1833–1848.
- Hornik, K. (1991). Approximation capabilities of multilayer feedforward networks. Neural Networks, 4(2), 251–257.
Paper not yet in RePEc: Add citation now
Huge, B., & Savine, A. (2020, September). Differential machine learning: The shape of things to come. Risk Magazine.
- Huré, C., Pham, H., & Warin, C. (2020). Deep backward schemes for high‐dimensional nonlinear PDEs. Mathematics of Computation, 89(324), 1547–1579.
Paper not yet in RePEc: Add citation now
- Kidger, P., & Lyons, T. (2020). Universal approximation with deep narrow networks. In Conference on Learning Theory, (pp. 2306–2327). Proceedings of Machine Learning Research.
Paper not yet in RePEc: Add citation now
- Kingma, D. P., & Ba, J. (2014). Adam: A method for stochastic optimization. arXiv:1412.6980.
Paper not yet in RePEc: Add citation now
- Lei, Y., Hu, T., Li, G., & Tang, K. (2019). Stochastic gradient descent for nonconvex learning without bounded gradient assumptions. IEEE Transactions on Neural Networks and Learning Systems, 31(10), 4394–4400.
Paper not yet in RePEc: Add citation now
Longstaff, F. A., & Schwartz, E. S. (2001). Valuing American options by simulation: A simple least‐squares approach. The Review of Financial Studies, 14(1), 113–147.
- Murphy, K. (2012). Machine learning: A probabilistic perspective. The MIT Press.
Paper not yet in RePEc: Add citation now
- NVIDIA Corporation (2020). Programming guide: Cuda toolkit documentation. https://docs.nvidia.com/cuda/cuda‐c‐programming‐guide/index.html.
Paper not yet in RePEc: Add citation now
- Pan, S. J., & Yang, Q. (2009). A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359.
Paper not yet in RePEc: Add citation now
- Rasmussen, C., & Williams, C. (2006). Gaussian processes for machine learning. Adaptive computation and machine learning. MIT Press.
Paper not yet in RePEc: Add citation now
- Recht, B., Re, C., Wright, S., & Niu, F. (2011). Hogwild!: A lock‐free approach to parallelizing stochastic gradient descent. In Advances in Neural Information Processing Systems, 24.
Paper not yet in RePEc: Add citation now
- Rockafellar, R., & Uryasev, S. (2000). Optimization of conditional value‐at‐risk. Journal of Risk, 2, 21–42.
Paper not yet in RePEc: Add citation now
- Shapiro, A., Dentcheva, D., & Ruszczyński, A. (2014). Lectures on stochastic programming: Modeling and theory. SIAM.
Paper not yet in RePEc: Add citation now
- Shorten, C., & Khoshgoftaar, T. (2019). A survey on image data augmentation for deep learning. Journal of Big Data, 6(1), 60.
Paper not yet in RePEc: Add citation now
- Tsitsiklis, J. N., & Van Roy, B. (2001). Regression methods for pricing complex American‐style options. IEEE Transactions on Neural Networks, 12(4), 694–703.
Paper not yet in RePEc: Add citation now
- Vershynin, R. (2018). High‐dimensional probability: An introduction with applications in data science, (Vol. 47). Cambridge University Press.
Paper not yet in RePEc: Add citation now