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Synthetic data is an emerging technology that can significantly accelerate the development and deployment of AI machine learning pipelines.
Uniqueness for the signature of a path of bounded variation and the reduced path group
Ben Hambly and Terry Lyons · 2010
Earlier work this paper cites.
Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
Earlier work this paper cites.
The uniqueness of signature problem in the non-markov setting
Horatio Boedihardjo and Xi Geng · 2015
Earlier work this paper cites.
Characteristic functions of measures on geometric rough paths
Ilya Chevyrev and Terry Lyons · 2016
Earlier work this paper cites.
Constantinos Daskalakis, Andrew Ilyas, Vasilis Syrgkanis, and Haoyang Zeng · 2017
Earlier work this paper cites.
Are gans created equal? a large-scale study
Mario Lucic, Karol Kurach, Marcin Michalski, Sylvain Gelly, and Olivier Bousquet · 2017
Earlier work this paper cites.
The limit points of (optimistic) gradient descent in min-max optimization
Constantinos Daskalakis and Ioannis Panageas · 2018
Earlier work this paper cites.
Cycles in adversarial regularized learning
Panayotis Mertikopoulos, Christos Papadimitriou, and Georgios Piliouras · 2018
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Signature moments to characterize laws of stochastic processes
Ilya Chevyrev and Harald Oberhauser · 2018
Earlier work this paper cites.
Privacy and synthetic datasets
Steven M Bellovin, Preetam K Dutta, and Nathan Reitinger · 2019
Cited alongside, same era.
Deep hedging: learning to simulate equity option markets
Magnus Wiese, Lianjun Bai, Ben Wood, and Hans Buehler · 2019
Cited alongside, same era.
On finding local nash equilibria (and only local nash equilibria) in zero-sum games
Eric V Mazumdar, Michael I Jordan, and S Shankar Sastry · 2019
Cited alongside, same era.
Learning stochastic differential equations using rnn with log signature features
Shujian Liao, Terry Lyons, Weixin Yang, and Hao Ni · 2019
Cited alongside, same era.
A generative adversarial network approach to calibration of local stochastic volatility models
Christa Cuchiero, Wahid Khosrawi, and Josef Teichmann · 2020
Cited alongside, same era.
Quant gans: deep generation of financial time series
Magnus Wiese, Robert Knobloch, Ralf Korn, and Peter Kretschmer · 2020
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Sig-sdes model for quantitative finance
Imanol Perez Arribas, Cristopher Salvi, and Lukasz Szpruch · 2020
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Neural controlled differential equations for irregular time series
Patrick Kidger, James Morrill, James Foster, and Terry Lyons · 2020
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Conditional sig-wasserstein gans for time series generation
Hao Ni, Lukasz Szpruch, Magnus Wiese, Shujian Liao, and Baoren Xiao · 2020
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A generalised signature method for multivariate time series feature extraction
James Morrill, Adeline Fermanian, Patrick Kidger, and Terry Lyons · 2020
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A data-driven market simulator for small data environments, 2020
Hans Buehler, Blanka Horvath, Terry Lyons, Imanol Perez Arribas, and Ben Wood · 2020
Cited alongside, same era.
Generative adversarial networks for financial trading strategies fine-tuning and combination
Adriano Koshiyama, Nick Firoozye, and Philip Treleaven · 2020
Cited alongside, same era.
Robust pricing and hedging via neural sdes
Patryk Gierjatowicz, Marc Sabate-Vidales, David Siska, Lukasz Szpruch, and Zan Zuric · 2020
Cited alongside, same era.
Generating synthetic data in finance: opportunities, challenges and pitfalls
Samuel Assefa, Danial Dervovic, Mahmoud Mahfouz, Tucker Balch, Prashant Reddy, and Manuela Veloso
Cited in the paper.
On gradient descent ascent for nonconvex-concave minimax problems
Tianyi Lin, Chi Jin, and Michael Jordan · 2020
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Gans may have no nash equilibria
Farzan Farnia and Asuman Ozdaglar · 2020
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Logsig-rnn: a novel network for robust and efficient skeleton-based action recognition
Shujian Liao, Terry Lyons, Weixin Yang, Kevin Schlegel, and Hao Ni · 2021
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