Fetching the paper…
Reading the bibliography…
Many modern computational approaches to classical problems in quantitative finance are formulated as empirical loss minimization (ERM), allowing direct applications of classical results from statistical machine learning.
Solving high-dimensional optimal stopping problems using deep learning
S. Becker, P. Cheridito, A. Jentzen, and T. Welti · 1908
Earlier work this paper cites.
Approximations by superpositions of a sigmoidal function
G. Cybenko · 1989
Earlier work this paper cites.
Approximation capabilities of multilayer feedforward networks
K. Hornik · 1991
Earlier work this paper cites.
Neural networks and the bias-variance dilemma
S. Geman, E. Bienenstock, and R. Doursat · 1992
Earlier work this paper cites.
Neuro-dynamic programming
D. P. Bertsekas and J. N. Tsitsiklis · 1996
Earlier work this paper cites.
Model selection and error estimation
P. L. Bartlett, S. Boucheron, and G. Lugosi · 2000
Earlier work this paper cites.
Numerical methods for stochastic control problems in continuous time , volume 24
P. Dupuis and H. J. Kushner · 2001
Earlier work this paper cites.
Rademacher and Gaussian complexities: Risk bounds and structural results
P. L. Bartlett and S. Mendelson · 2002
Earlier work this paper cites.
Model selection and error estimation
P. L. Bartlett, S. Boucheron, and G. Lugosi · 2002
Earlier work this paper cites.
Empirical margin distributions and bounding the generalization error of combined classifiers
V. Koltchinskii and D. Panchenko · 2002
Earlier work this paper cites.
Introduction to statistical learning theory
O. Bousquet, S. Boucheron, and G. Lugosi · 2003
Earlier work this paper cites.
Hedging with neural networks
J. Ruf and W. Wang · 2004
Earlier work this paper cites.
Theory of classification: A survey of some recent advances
S. Boucheron, O. Bousquet, and G. Lugosi · 2005
Earlier work this paper cites.
Controlled Markov processes and viscosity solutions , volume 25
W. H. Fleming and H. M. Soner · 2006
Earlier work this paper cites.
Theory of point estimation
E. L. Lehmann and G. Casella · 2006
Earlier work this paper cites.
On the generalization ability of online strongly convex programming algorithms
S. M. Kakade and A. Tewari · 2009
Earlier work this paper cites.
An introduction to statistical learning , volume 112
G. James, D. Witten, T. Hastie, and R. Tibshirani · 2013
Earlier work this paper cites.
Adam: A method for stochastic optimization
D. P. Kingma and J. Ba · 2014
Cited alongside, same era.
Understanding machine learning: From theory to algorithms
S. Shalev-Shwartz and S. Ben-David · 2014
Cited alongside, same era.
On the rate of convergence in Wasserstein distance of the empirical measure
N. Fournier and A. Guillin · 2015
Cited alongside, same era.
Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
K. He, X. Zhang, S. Ren, and J. Sun · 2015
Cited alongside, same era.
Norm-based capacity control in neural networks
B. Neyshabur, R. Tomioka, and N. Srebro · 2015
Cited alongside, same era.
Deep learning approximation for stochastic control problems
J. Han and W. E · 2016
Cited alongside, same era.
Dgm: A deep learning algorithm for solving partial differential equations
J. Sirignano and K. Spiliopoulos · 2018
Later among the works it cites.
Owl: A general-purpose numerical library in OCaml
L. Wang · 2018
Later among the works it cites.
Mathematical foundations of supervised learning
M. M. Wolf · 2018
Later among the works it cites.
Reconciling modern machine-learning practice and the classical bias–variance trade-off
M. Belkin, D. Hsu, S. Ma, and S. Mandal · 2019
Later among the works it cites.
Two-stage sample robust optimization
D. Bertsimas, S. Shtern, and B. Sturt · 2019
Later among the works it cites.
Machine learning for semi linear pdes
Q. Chan-Wai-Nam, J. Mikael, and X. Warin · 2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
From empirical observations to tree models for stochastic optimization: convergence properties
G. C. Pflug and A. Pichler · 2016
Cited alongside, same era.
Deep primal-dual algorithm for BSDEs: Applications of machine learning to CVA and IM
P. Henry-Labordère · 2017
Cited alongside, same era.
Understanding deep learning requires rethinking generalization
C. Zhang, S. Bengio, M. Hardt, B. Recht, and O. Vinyals · 2017
Cited alongside, same era.
Deep neural networks algorithms for stochastic control problems on finite horizon, part 2: Numerical applications
A. Bachouch, C. Huré, N. Langrené, and H. Pham · 2018
Cited alongside, same era.
A data-driven approach for multi-stage linear optimization
D. Bertsimas, S. Shtern, and B. Sturt · 2018
Cited alongside, same era.
Data-driven distributionally robust optimization using the Wasserstein metric: Performance guarantees and tractable reformulations
P. M. Esfahani and D. Kuhn · 2018
Cited alongside, same era.
Asset pricing with general transaction costs: Theory and numerics
L. Gonon, J. Muhle-Karbe, and X. Shi · 2019
Later among the works it cites.
Some machine learning schemes for high-dimensional nonlinear pdes
C. Huré, H. Pham, and X. Warin · 2019
Later among the works it cites.
Generating synthetic data in finance: opportunities, challenges and pitfalls
S. A. Assefa, D. Dervovic, M. Mahfouz, R. E. Tillman, P. Reddy, and M. Veloso · 2020
Closest in time.
Computational aspects of robust optimized certainty equivalents and option pricing
D. Bartl, S. Drapeau, and L. Tangpi · 2020
Closest in time.
A generative adversarial network approach to calibration of local stochastic volatility models
C. Cuchiero, W. Khosrawi, and J. Teichmann · 2020
Closest in time.
Deep learning for discrete-time hedging in incomplete markets
S. Fecamp, J. Mikael, and X. Warin · 2020
Closest in time.
Size-independent sample complexity of neural networks
N. Golowich, A. Rakhlin, and O. Shamir · 2020
Closest in time.
The OCaml system release 4.10 , 2 2020
X. Leroy, D. Doligez, A. Frisch, J. Garrigue, D. Rémy, and J. Vouillon · 2020
Closest in time.
On Monte-Carlo methods in convex stochastic optimization
D. Bartl and S. Mendelson · 2021
Closest in time.
Estimating processes in adapted Wasserstein distance
J. Backhoff, D. Bartl, M. Beiglböck, and J. Wiesel · 2022
Closest in time.