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Utility Theory of Synthetic Data Generation
v1v2v3v4 (latest)

Utility Theory of Synthetic Data Generation

17 May 2023
Shi Xu
W. Sun
Guang Cheng
ArXiv (abs)PDFHTML

Papers citing "Utility Theory of Synthetic Data Generation"

41 / 41 papers shown
Enhancing Obsolescence Forecasting with Deep Generative Data Augmentation: A Semi-Supervised Framework for Low-Data Industrial Applications
Enhancing Obsolescence Forecasting with Deep Generative Data Augmentation: A Semi-Supervised Framework for Low-Data Industrial Applications
Elie Saad
Mariem Besbes
Marc Zolghadri
Victor Czmil
Claude Baron
Vincent Bourgeois
262
0
0
02 May 2025
GReaTER: Generate Realistic Tabular data after data Enhancement and Reduction
GReaTER: Generate Realistic Tabular data after data Enhancement and Reduction
Tung Sum Thomas Kwok
Chi-Hua Wang
Guang Cheng
LMTD
309
3
0
19 Mar 2025
A Theoretical Perspective: How to Prevent Model Collapse in Self-consuming Training Loops
A Theoretical Perspective: How to Prevent Model Collapse in Self-consuming Training LoopsInternational Conference on Learning Representations (ICLR), 2025
Shi Fu
Yingjie Wang
Yuzhu Chen
Xinmei Tian
Dacheng Tao
376
8
0
26 Feb 2025
On the optimal approximation of Sobolev and Besov functions using deep ReLU neural networks
On the optimal approximation of Sobolev and Besov functions using deep ReLU neural networksApplied and Computational Harmonic Analysis (ACHA), 2024
Yunfei Yang
396
4
0
02 Sep 2024
A Survey on Statistical Theory of Deep Learning: Approximation, Training
  Dynamics, and Generative Models
A Survey on Statistical Theory of Deep Learning: Approximation, Training Dynamics, and Generative ModelsAnnual Review of Statistics and Its Application (ARSIA), 2024
Namjoon Suh
Guang Cheng
MedIm
355
19
0
14 Jan 2024
Downstream Task-Oriented Generative Model Selections on Synthetic Data
  Training for Fraud Detection Models
Downstream Task-Oriented Generative Model Selections on Synthetic Data Training for Fraud Detection Models
Yinan Cheng
ChiHua Wang
Vamsi K. Potluru
T. Balch
Guang Cheng
184
9
0
01 Jan 2024
Dataset Diffusion: Diffusion-based Synthetic Dataset Generation for
  Pixel-Level Semantic Segmentation
Dataset Diffusion: Diffusion-based Synthetic Dataset Generation for Pixel-Level Semantic SegmentationNeural Information Processing Systems (NeurIPS), 2023
Quang H. Nguyen
T. Vu
Anh Tran
Kim Dan Nguyen
DiffM
483
137
0
25 Sep 2023
Realistic Synthetic Financial Transactions for Anti-Money Laundering
  Models
Realistic Synthetic Financial Transactions for Anti-Money Laundering ModelsNeural Information Processing Systems (NeurIPS), 2023
Erik Altman
Jovan Blanuvsa
Luc von Niederhäusern
Béni Egressy
Andreea Anghel
Kubilay Atasu
350
77
0
22 Jun 2023
Synthetic data, real errors: how (not) to publish and use synthetic data
Synthetic data, real errors: how (not) to publish and use synthetic dataInternational Conference on Machine Learning (ICML), 2023
B. V. Breugel
Zhaozhi Qian
M. Schaar
SyDa
280
42
0
16 May 2023
Synthetic Data from Diffusion Models Improves ImageNet Classification
Synthetic Data from Diffusion Models Improves ImageNet Classification
Shekoofeh Azizi
Simon Kornblith
Chitwan Saharia
Mohammad Norouzi
David J. Fleet
VLMDiffM
844
390
0
17 Apr 2023
Statistical Theory of Differentially Private Marginal-based Data
  Synthesis Algorithms
Statistical Theory of Differentially Private Marginal-based Data Synthesis AlgorithmsInternational Conference on Learning Representations (ICLR), 2023
Ximing Li
Chendi Wang
Guang Cheng
SyDa
334
10
0
21 Jan 2023
Optimal Approximation Rates for Deep ReLU Neural Networks on Sobolev and
  Besov Spaces
Optimal Approximation Rates for Deep ReLU Neural Networks on Sobolev and Besov SpacesJournal of machine learning research (JMLR), 2022
Jonathan W. Siegel
582
44
0
25 Nov 2022
Improving Adversarial Robustness by Contrastive Guided Diffusion Process
Improving Adversarial Robustness by Contrastive Guided Diffusion ProcessInternational Conference on Machine Learning (ICML), 2022
Yidong Ouyang
Liyan Xie
Guang Cheng
208
10
0
18 Oct 2022
Downstream Datasets Make Surprisingly Good Pretraining Corpora
Downstream Datasets Make Surprisingly Good Pretraining CorporaAnnual Meeting of the Association for Computational Linguistics (ACL), 2022
Kundan Krishna
Saurabh Garg
Jeffrey P. Bigham
Zachary Chase Lipton
221
37
0
28 Sep 2022
Synthetic Data -- what, why and how?
Synthetic Data -- what, why and how?
James Jordon
Lukasz Szpruch
F. Houssiau
M. Bottarelli
Giovanni Cherubin
Carsten Maple
Samuel N. Cohen
Adrian Weller
256
166
0
06 May 2022
Optimally tackling covariate shift in RKHS-based nonparametric
  regression
Optimally tackling covariate shift in RKHS-based nonparametric regressionAnnals of Statistics (Ann. Stat.), 2022
Cong Ma
Reese Pathak
Martin J. Wainwright
171
57
0
06 May 2022
Generative Adversarial Networks
Generative Adversarial NetworksInternational Conference on Computing Communication and Networking Technologies (ICCCNT), 2021
Gilad Cohen
Raja Giryes
GAN
816
30,401
0
01 Mar 2022
Distribution-Invariant Differential Privacy
Distribution-Invariant Differential Privacy
Xuan Bi
Xiaotong Shen
241
20
0
08 Nov 2021
A Deep Generative Approach to Conditional Sampling
A Deep Generative Approach to Conditional Sampling
Xingyu Zhou
Yuling Jiao
Jin Liu
Jian Huang
157
52
0
19 Oct 2021
Improving Robustness using Generated Data
Improving Robustness using Generated Data
Sven Gowal
Sylvestre-Alvise Rebuffi
Olivia Wiles
Florian Stimberg
D. A. Calian
Timothy A. Mann
388
351
0
18 Oct 2021
Synthetic Data Generation for Fraud Detection using GANs
Synthetic Data Generation for Fraud Detection using GANs
C. Charitou
S. Dragicevic
Artur Garcez
131
24
0
26 Sep 2021
Relaxed Marginal Consistency for Differentially Private Query Answering
Relaxed Marginal Consistency for Differentially Private Query Answering
Ryan McKenna
Siddhant Pradhan
Daniel Sheldon
G. Miklau
280
10
0
13 Sep 2021
Synthetic Data for Model Selection
Synthetic Data for Model SelectionInternational Conference on Machine Learning (ICML), 2021
Alon Shoshan
Nadav Bhonker
Igor Kviatkovsky
Matan Fintz
Gérard Medioni
152
7
0
03 May 2021
Maximum Likelihood Training of Score-Based Diffusion Models
Maximum Likelihood Training of Score-Based Diffusion ModelsNeural Information Processing Systems (NeurIPS), 2021
Yang Song
Conor Durkan
Iain Murray
Stefano Ermon
DiffM
788
804
0
22 Jan 2021
Non-Negative Bregman Divergence Minimization for Deep Direct Density
  Ratio Estimation
Non-Negative Bregman Divergence Minimization for Deep Direct Density Ratio EstimationInternational Conference on Machine Learning (ICML), 2020
Masahiro Kato
Takeshi Teshima
368
49
0
12 Jun 2020
Decision-Making with Auto-Encoding Variational Bayes
Decision-Making with Auto-Encoding Variational BayesNeural Information Processing Systems (NeurIPS), 2020
Romain Lopez
Pierre Boyeau
Nir Yosef
Michael I. Jordan
Jeffrey Regier
BDL
1.5K
20,656
0
17 Feb 2020
Modeling Tabular data using Conditional GAN
Modeling Tabular data using Conditional GANNeural Information Processing Systems (NeurIPS), 2019
Lei Xu
Maria Skoularidou
Alfredo Cuesta-Infante
K. Veeramachaneni
CMLMUSyDaGAN
497
1,639
0
01 Jul 2019
Unlabeled Data Improves Adversarial Robustness
Unlabeled Data Improves Adversarial RobustnessNeural Information Processing Systems (NeurIPS), 2019
Y. Carmon
Aditi Raghunathan
Ludwig Schmidt
Abigail Z. Jacobs
John C. Duchi
493
793
0
31 May 2019
SynC: A Unified Framework for Generating Synthetic Population with
  Gaussian Copula
SynC: A Unified Framework for Generating Synthetic Population with Gaussian Copula
Colin Wan
Zheng Li
Alicia Guo
Yue Zhao
SyDa
133
8
0
16 Apr 2019
How Well Generative Adversarial Networks Learn Distributions
How Well Generative Adversarial Networks Learn DistributionsJournal of machine learning research (JMLR), 2018
Tengyuan Liang
GAN
325
110
0
07 Nov 2018
Measuring the quality of Synthetic data for use in competitions
Measuring the quality of Synthetic data for use in competitions
James Jordon
Chang Jo Kim
M. Schaar
105
33
0
29 Jun 2018
Training Deep Networks with Synthetic Data: Bridging the Reality Gap by
  Domain Randomization
Training Deep Networks with Synthetic Data: Bridging the Reality Gap by Domain Randomization
Jonathan Tremblay
Aayush Prakash
David Acuna
M. Brophy
Varun Jampani
Cem Anil
Thang To
Eric Cameracci
Shaad Boochoon
Stan Birchfield
OOD
442
926
0
18 Apr 2018
Differentially Private Generative Adversarial Network
Differentially Private Generative Adversarial Network
Liyang Xie
Kaixiang Lin
Shu Wang
Fei Wang
Jiayu Zhou
SyDa
227
557
0
19 Feb 2018
Synthetic Data Augmentation using GAN for Improved Liver Lesion
  Classification
Synthetic Data Augmentation using GAN for Improved Liver Lesion Classification
Maayan Frid-Adar
Eyal Klang
Michal Amitai
Jacob Goldberger
H. Greenspan
MedImGAN
222
800
0
08 Jan 2018
Nonparametric regression using deep neural networks with ReLU activation
  function
Nonparametric regression using deep neural networks with ReLU activation function
Johannes Schmidt-Hieber
639
928
0
22 Aug 2017
Least Squares Generative Adversarial Networks
Least Squares Generative Adversarial Networks
Xudong Mao
Qing Li
Haoran Xie
Raymond Y. K. Lau
Zhen Wang
Stephen Paul Smolley
GAN
882
4,897
0
13 Nov 2016
The Normal Law Under Linear Restrictions: Simulation and Estimation via
  Minimax Tilting
The Normal Law Under Linear Restrictions: Simulation and Estimation via Minimax Tilting
Z. Botev
132
260
0
14 Mar 2016
Conditional Generative Adversarial Nets
Conditional Generative Adversarial Nets
M. Berk Mirza
Simon Osindero
GANSyDaAI4CE
967
11,281
0
06 Nov 2014
What Regularized Auto-Encoders Learn from the Data Generating
  Distribution
What Regularized Auto-Encoders Learn from the Data Generating DistributionJournal of machine learning research (JMLR), 2012
Guillaume Alain
Yoshua Bengio
OODDRL
292
530
0
18 Nov 2012
Choice of neighbor order in nearest-neighbor classification
Choice of neighbor order in nearest-neighbor classification
P. Hall
B. Park
R. Samworth
426
329
0
29 Oct 2008
Fast learning rates for plug-in classifiers
Fast learning rates for plug-in classifiers
Jean-Yves Audibert
Alexandre B. Tsybakov
985
494
0
17 Aug 2007
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