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Synthetic data has made tremendous strides in various commercial settings including finance, healthcare, and virtual reality.
Essentially, all models are wrong, but some are useful
George EP Box and Norman R Draper · 1919
Earlier work this paper cites.
On the theory of the brownian motion
George E Uhlenbeck and Leonard S Ornstein · 1930
Earlier work this paper cites.
Some statistical methods connected with series of events
David R Cox · 1955
Earlier work this paper cites.
A new approach to linear filtering and prediction problems
Rudolph Emil Kalman · 1960
Earlier work this paper cites.
Statistical inference for probabilistic functions of finite state markov chains
Leonard E Baum and Ted Petrie · 1966
Earlier work this paper cites.
Spectra of some self-exciting and mutually exciting point processes
Alan G Hawkes · 1971
Earlier work this paper cites.
The coincidence approach to stochastic point processes
Odile Macchi · 1975
Earlier work this paper cites.
A self-correcting point process
Valerie Isham and Mark Westcott · 1979
Earlier work this paper cites.
Point processes
David Roxbee Cox and Valerie Isham · 1980
Earlier work this paper cites.
On lewis’ simulation method for point processes
Yosihiko Ogata · 1981
Earlier work this paper cites.
Arima model building and the time series analysis approach to forecasting
Paul Newbold · 1983
Earlier work this paper cites.
Real applications of markov decision processes
Douglas J White · 1985
Earlier work this paper cites.
An introduction to hidden markov models
Lawrence Rabiner and Biinghwang Juang · 1986
Earlier work this paper cites.
Knowledge based simulation: an artificial intelligence approach to system modeling and automating the simulation life cycle
Mark Fox, Nizwer Husain, Malcolm McRoberts, and YV Reddy · 1988
Earlier work this paper cites.
Multilayer feedforward networks are universal approximators
Kurt Hornik, Maxwell Stinchcombe, and Halbert White · 1989
Earlier work this paper cites.
Financial models and simulation
D Chorafas · 1995
Earlier work this paper cites.
An introduction to the kalman filter
Greg Welch, Gary Bishop, et al · 1995
Earlier work this paper cites.
Learning dynamic bayesian networks
Zoubin Ghahramani · 1997
Earlier work this paper cites.
Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber · 1997
Earlier work this paper cites.
Space-time point-process models for earthquake occurrences
Yosihiko Ogata · 1998
Earlier work this paper cites.
Smote: synthetic minority over-sampling technique
Nitesh V Chawla, Kevin W Bowyer, Lawrence O Hall, and W Philip Kegelmeyer · 2002
Earlier work this paper cites.
Dynamic bayesian networks: representation, inference and learning
Kevin Patrick Murphy · 2002
Earlier work this paper cites.
An introduction to the theory of point processes: volume I: elementary theory and methods
Daryl J Daley, David Vere-Jones, et al · 2003
Earlier work this paper cites.
Energy-based models for sparse overcomplete representations
Yee Whye Teh, Max Welling, Simon Osindero, and Geoffrey E Hinton · 2003
Earlier work this paper cites.
Automated Planning. Theory & Practice
Malik Ghallab, Dana Nau, and Paolo Traverso · 2004
Earlier work this paper cites.
Agent-based computational finance
Blake LeBaron · 2006
Earlier work this paper cites.
How to break anonymity of the netflix prize dataset, 2007
Arvind Narayanan and Vitaly Shmatikov · 2007
Earlier work this paper cites.
Generating synthetic data to match data mining patterns
Josh Eno and Craig W Thompson · 2008
Earlier work this paper cites.
Simulation and inference for stochastic differential equations: with R examples
Stefano M Iacus et al · 2008
Earlier work this paper cites.
Visualizing data using t-sne
Laurens van der Maaten and Geoffrey Hinton · 2008
Earlier work this paper cites.
Ornstein–uhlenbeck processes and extensions
Ross A Maller, Gernot Müller, and Alex Szimayer · 2009
Earlier work this paper cites.
Rare event simulation using Monte Carlo methods
Gerardo Rubino, Bruno Tuffin, et al · 2009
Earlier work this paper cites.
Random forests for generating partially synthetic, categorical data
Gregory Caiola and Jerome P Reiter · 2010
Earlier work this paper cites.
Digital signatures
Jonathan Katz · 2010
Earlier work this paper cites.
A taxonomy of sequential pattern mining algorithms
Nizar R Mabroukeh and Christie I Ezeife · 2010
Earlier work this paper cites.
Relational dynamic influence diagram language (rddl): Language description
Scott Sanner et al · 2010
Earlier work this paper cites.
Markov decision processes with applications to finance
Nicole Bäuerle and Ulrich Rieder · 2011
Earlier work this paper cites.
Daniel Borrajo, Manuela Veloso, and Sameena Shah · 2011
Earlier work this paper cites.
Market making and mean reversion
Tanmoy Chakraborty and Michael Kearns · 2011
Earlier work this paper cites.
Practical variational inference for neural networks
Alex Graves · 2011
Earlier work this paper cites.
Extended k-anonymity models against sensitive attribute disclosure
Xiaoxun Sun, Lili Sun, and Hua Wang · 2011
Earlier work this paper cites.
Gaussian copula marginal regression
Guido Masarotto and Cristiano Varin · 2012
Earlier work this paper cites.
Introduction to stochastic processes
Erhan Cinlar · 2013
Earlier work this paper cites.
Exact simulation of Hawkes process with exponentially decaying intensity
Angelos Dassios and Hongbiao Zhao · 2013
Earlier work this paper cites.
Modelling extremal events: for insurance and finance
Paul Embrechts, Claudia Klüppelberg, and Thomas Mikosch · 2013
Earlier work this paper cites.
Handbook in Monte Carlo simulation: applications in financial engineering, risk management, and economics
Paolo Brandimarte · 2014
Earlier work this paper cites.
The algorithmic foundations of differential privacy
Cynthia Dwork, Aaron Roth, et al · 2014
Earlier work this paper cites.
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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.
Generative adversarial networks, 2014
Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
Earlier work this paper cites.
Differentially private synthesization of multi-dimensional data using copula functions
Haoran Li, Li Xiong, and Xiaoqian Jiang · 2014
Earlier work this paper cites.
Markov decision processes: discrete stochastic dynamic programming
Martin L Puterman · 2014
Earlier work this paper cites.
Markov decision processes: discrete stochastic dynamic programming
Martin L Puterman · 2014
Earlier work this paper cites.
Multi-task multi-dimensional hawkes processes for modeling event sequences
Dixin Luo, Hongteng Xu, Yi Zhen, Xia Ning, Hongyuan Zha, Xiaokang Yang, and Wenjun Zhang · 2015
Earlier work this paper cites.
Deep unsupervised learning using nonequilibrium thermodynamics
Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli · 2015
Earlier work this paper cites.
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Earlier work this paper cites.
Deep learning with differential privacy
Martin Abadi, Andy Chu, Ian Goodfellow, H. Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang · 2016
Earlier work this paper cites.
Xgboost: A scalable tree boosting system
Tianqi Chen and Carlos Guestrin · 2016
Earlier work this paper cites.
Tutorial on variational autoencoders
Carl Doersch · 2016
Earlier work this paper cites.
Dropout as a bayesian approximation: Representing model uncertainty in deep learning
Yarin Gal and Zoubin Ghahramani · 2016
Earlier work this paper cites.
Nips 2016 tutorial: Generative adversarial networks
Ian Goodfellow · 2016
Earlier work this paper cites.
Partially observed Markov decision processes
Vikram Krishnamurthy · 2016
Earlier work this paper cites.
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Earlier work this paper cites.
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Takanobu Mizuta · 2016
Earlier work this paper cites.
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Aaron van den Oord, Sander Dieleman, Heiga Zen, Karen Simonyan, Oriol Vinyals, Alex Graves, Nal Kalchbrenner, Andrew Senior, and Koray Kavukcuoglu · 2016
Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
Objective-reinforced generative adversarial networks (organ) for sequence generation models
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