Fetching the paper…
Reading the bibliography…
Generating synthetic data through generative models is gaining interest in the ML community and beyond, promising a future where datasets can be tailored to individual needs.
Modeling Tabular data using Conditional GAN
Xu, L., Skoularidou, M., Cuesta-Infante, A., and Veeramachaneni, K · 1907
Earlier work this paper cites.
Uncertainty Quantification with Generative Models
Böhm, V., Lanusse, F., and Seljak, U · 1910
Earlier work this paper cites.
Deep Ensembles: A Loss Landscape Perspective
Fort, S., Hu, H., and Lakshminarayanan, B · 1912
Earlier work this paper cites.
The Elements of Statistical Learning
Hastie, T., Friedman, J., and Tibshirani, R · 2001
Earlier work this paper cites.
SMOTE: Synthetic Minority Over-sampling Technique
Chawla, N. V., Bowyer, K. W., Hall, L. O., and Kegelmeyer, W. P · 2002
Earlier work this paper cites.
BatchEnsemble: An Alternative Approach to Efficient Ensemble and Lifelong Learning
Wen, Y., Tran, D., and Ba, J · 2002
Earlier work this paper cites.
Our data, ourselves: Privacy via distributed noise generation
Dwork, C., Kenthapadi, K., McSherry, F., Mironov, I., and Naor, M · 2006
Earlier work this paper cites.
Denoising Diffusion Probabilistic Models
Ho, J., Jain, A., and Abbeel, P · 2006
Earlier work this paper cites.
Hyperparameter Ensembles for Robustness and Uncertainty Quantification
Wenzel, F., Snoek, J., Tran, D., and Jenatton, R · 2006
Earlier work this paper cites.
UCI machine learning repository, 2007
Asuncion, A. and Newman, D · 2007
Earlier work this paper cites.
Generative Adversarial Networks
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y · 2014
Earlier work this paper cites.
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
Gal, Y. and Ghahramani, Z · 2015
Earlier work this paper cites.
A note on the evaluation of generative models
Theis, L., Van Den Oord, A., and Bethge, M · 2015
Earlier work this paper cites.
The surveillance, epidemiology and end results (SEER) program and pathology: towards strengthening the critical relationship
Duggan, M. A., Anderson, W. F., Altekruse, S., Penberthy, L., and Sherman, M. E · 2016
Earlier work this paper cites.
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles
Lakshminarayanan, B., Pritzel, A., and Blundell, C · 2016
Earlier work this paper cites.
Data Augmentation Generative Adversarial Networks
Antoniou, A., Storkey, A., and Edwards, H · 2017
Earlier work this paper cites.
Multi-Agent Diverse Generative Adversarial Networks
Ghosh, A., Kulharia, V., Namboodiri, V., Torr, P. H., and Dokania, P. K · 2017
Cited alongside, same era.
Grover, A. and Ermon, S · 2017
Cited alongside, same era.
Kaggle machine learning and data science survey, 2017
Kaggle · 2017
Cited alongside, same era.
Dual Discriminator Generative Adversarial Nets
Nguyen, T. D., Le, T., Vu, H., and Phung, D · 2017
Cited alongside, same era.
AdaGAN: Boosting Generative Models
Tolstikhin, I., Gelly, S., Bousquet, O., Simon-Gabriel, C. J., and Schölkopf, B · 2017
Cited alongside, same era.
A review of uncertainty quantification in deep learning: Techniques, applications and challenges
Abdar, M., Pourpanah, F., Hussain, S., Rezazadegan, D., Liu, L., Ghavamzadeh, M., Fieguth, P., Cao, X., Khosravi, A., Acharya, U. R., Makarenkov, V., and Nahavandi, S · 2021
Later among the works it cites.
Deep neural networks and tabular data: A survey
Borisov, V., Leemann, T., Seßler, K., Haug, J., Pawelczyk, M., and Kasneci, G · 2021
Later among the works it cites.
DP-GAN: Differentially private consecutive data publishing using generative adversarial nets
Ho, S., Qu, Y., Gu, B., Gao, L., Li, J., and Xiang, Y · 2021
Later among the works it cites.
Hide-and-Seek Privacy Challenge: Synthetic Data Generation vs. Patient Re-identification
Jordon, J., Jarrett, D., Saveliev, E., Yoon, J., Elbers, P., Thoral, P., Ercole, A., Zhang, C., Belgrave, D., and van der Schaar, M · 2021
Later among the works it cites.
Tabular data: Deep learning is not all you need
Shwartz-Ziv, R. and Armon, A · 2021
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
mixup: Beyond Empirical Risk Minimization
Zhang, H., Cisse, M., Dauphin, Y. N., and Lopez-Paz, D · 2017
Cited alongside, same era.
MGAN: Training Generative Adversarial Nets with Multiple Generators
Hoang, Q., Nguyen, T. D., Le, T., and Phung, D · 2018
Cited alongside, same era.
Dropout-GAN: Learning from a Dynamic Ensemble of Discriminators
Mordido, G., Yang, H., and Meinel, C · 2018
Cited alongside, same era.
Fairgan: Fairness-aware generative adversarial networks
Xu, D., Yuan, S., Zhang, L., and Wu, X · 2018
Cited alongside, same era.
Yoon, J., Jordon, J., and Van Der Schaar, M · 2018
Cited alongside, same era.
PATE-GAN: Generating Synthetic Data with Differential Privacy Guarantees
Jordon, J., Yoon, J., and Schaar, M · 2019
Cited alongside, same era.
Bayesian Uncertainty Quantification with Synthetic Data
Phan, B., Khan, S., Salay, R., and Czarnecki, K · 2019
Cited alongside, same era.
Later among the works it cites.
DECAF: Generating Fair Synthetic Data Using Causally-Aware Generative Networks
van Breugel, B., Kyono, T., Berrevoets, J., and van der Schaar, M · 2021
Later among the works it cites.
Deep Ensembles as Approximate Bayesian Inference, 10 2021
Wilson, A. G. and Izmailov, P · 2021
Later among the works it cites.
Alaa, A. M., van Breugel, B., Saveliev, E., and van der Schaar, M · 2022
Later among the works it cites.
Conditional Generation of Medical Time Series for Extrapolation to Underrepresented Populations
Bing, S., Dittadi, A., Bauer, S., and Schwab, P · 2022
Later among the works it cites.
Conditional synthetic data generation for robust machine learning applications with limited pandemic data
Das, H. P., Tran, R., Singh, J., Yue, X., Tison, G., Sangiovanni-Vincentelli, A., and Spanos, C. J · 2022
Later among the works it cites.
Dina, A. S., Siddique, A., and Manivannan, D · 2022
Later among the works it cites.
GFlowOut: Dropout with Generative Flow Networks
Liu, D., Jain, M., Dossou, B., Shen, Q., Lahlou, S., Goyal, A., Malkin, N., Emezue, C., Zhang, D., Hassen, N., Ji, X., Kawaguchi, K., and Bengio, Y · 2022
Later among the works it cites.
Synthcity: facilitating innovative use cases of synthetic data in different data modalities
Qian, Z., Cebere, B.-C., and van der Schaar, M · 2023
Closest in time.
Beyond privacy: Navigating the opportunities and challenges of synthetic data
van Breugel, B. and van der Schaar, M · 2023
Closest in time.
Membership inference attacks against synthetic data through overfitting detection
van Breugel, B., Sun, H., Qian, Z., and van der Schaar, M · 2023
Closest in time.