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Synthcity is an open-source software package for innovative use cases of synthetic data in ML fairness, privacy and augmentation across diverse tabular data modalities, including static data, regular and irregular time series, data with censoring, multi-source data, composite data, and more.
Yunhui Long, Boxin Wang, Zhuolin Yang, Bhavya Kailkhura, Aston Zhang, Carl A. Gunter, and Bo Li · 1906
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Revealing information while preserving privacy
Irit Dinur and Kobbi Nissim · 2003
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Differential privacy: A survey of results
Cynthia Dwork · 2008
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Causality
Judea Pearl · 2009
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Private traits and attributes are predictable from digital records of human behavior
Michal Kosinski, David Stillwell, and Thore Graepel · 2013
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Learning fair representations
Rich Zemel, Yu Wu, Kevin Swersky, Toni Pitassi, and Cynthia Dwork · 2013
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Deep Learning with Differential Privacy
Martín Abadi, Andy Chu, Ian Goodfellow, H Brendan Mcmahan, Ilya Mironov, Kunal Talwar, and Li Zhang · 2016
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Data Augmentation Generative Adversarial Networks
Antreas Antoniou, Amos Storkey, and Harrison Edwards · 2017
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Counterfactual fairness
Matt J Kusner, Joshua Loftus, Chris Russell, and Ricardo Silva · 2017
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Membership inference attacks against machine learning models
Reza Shokri, Marco Stronati, Congzheng Song, and Vitaly Shmatikov · 2017
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Courts Are Using AI to Sentence Criminals. That Must Stop Now
Jason Tashea · 2017
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Unpaired image-to-image translation using cycle-consistent adversarial networks
Jun-Yan Zhu, Taesung Park, Phillip Isola, and Alexei A Efros · 2017
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Amazon scraps secret AI recruiting tool that showed bias against women
Jeffrey Dastin · 2018
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Potential biases in machine learning algorithms using electronic health record data
Milena A Gianfrancesco, Suzanne Tamang, Jinoos Yazdany, and Gabriela Schmajuk · 2018
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Pate-gan: Generating synthetic data with differential privacy guarantees
James Jordon, Jinsung Yoon, and Mihaela Van Der Schaar · 2018
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Gender Bias in Neural Natural Language Processing
Kaiji Lu, Piotr Mardziel, Fangjing Wu, Preetam Amancharla, and Anupam Datta · 2018
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General and specific utility measures for synthetic data
Joshua Snoke, Gillian M Raab, Beata Nowok, Chris Dibben, and Aleksandra Slavkovic · 2018
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Dp-gan: Differentially private consecutive data publishing using generative adversarial nets
Stella Ho, Youyang Qu, Bruce Gu, Longxiang Gao, Jianxin Li, and Yong Xiang · 2021
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Achieving fairness in medical devices
Achuta Kadambi · 2021
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A survey on bias and fairness in machine learning
Ninareh Mehrabi, Fred Morstatter, Nripsuta Saxena, Kristina Lerman, and Aram Galstyan · 2021
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“everyone wants to do the model work, not the data work”: Data cascades in high-stakes ai
Nithya Sambasivan, Shivani Kapania, Hannah Highfill, Diana Akrong, Praveen Paritosh, and Lora M Aroyo · 2021
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Generative adversarial networks (gans) challenges, solutions, and future directions
Divya Saxena and Jiannong Cao · 2021
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Decaf: Generating fair synthetic data using causally-aware generative networks
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Depeng Xu, Shuhan Yuan, Lu Zhang, and Xintao Wu · 2018
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Radialgan: Leveraging multiple datasets to improve target-specific predictive models using generative adversarial networks
Jinsung Yoon, James Jordon, and Mihaela Schaar · 2018
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Privacy-preserving generative deep neural networks support clinical data sharing
Brett K Beaulieu-Jones, Zhiwei Steven Wu, Chris Williams, Ran Lee, Sanjeev P Bhavnani, James Brian Byrd, and Casey S Greene · 2019
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Achieving causal fairness through generative adversarial networks
Depeng Xu, Yongkai Wu, Shuhan Yuan, Lu Zhang, and Xintao Wu · 2019
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Anonymization through data synthesis using generative adversarial networks (ads-gan)
Jinsung Yoon, Lydia N Drumright, and Mihaela Van Der Schaar · 2020
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Sam Bond-Taylor, Adam Leach, Yang Long, and Chris G. Willcocks · 2021
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Stereotype and Skew: Quantifying Gender Bias in Pre-trained and Fine-tuned Language Models
Daniel de Vassimon Manela, David Errington, Thomas Fisher, Boris van Breugel, and Pasquale Minervini · 2021
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Boris van Breugel, Trent Kyono, Jeroen Berrevoets, and Mihaela van der Schaar · 2021
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Conditional Generation of Medical Time Series for Extrapolation to Underrepresented Populations
Simon Bing, Andrea Dittadi, Stefan Bauer, and Patrick Schwab · 2022
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Conditional synthetic data generation for robust machine learning applications with limited pandemic data
Hari Prasanna Das, Ryan Tran, Japjot Singh, Xiangyu Yue, Geoffrey Tison, Alberto Sangiovanni-Vincentelli, and Costas J Spanos · 2022
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Ayesha S. Dina, A. B. Siddique, and D. Manivannan · 2022
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Hyperimpute: Generalized iterative imputation with automatic model selection
Daniel Jarrett, Bogdan C Cebere, Tennison Liu, Alicia Curth, and Mihaela van der Schaar · 2022
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Synthetic Data – what, why and how?
James Jordon, Lukasz Szpruch, Florimond Houssiau, Mirko Bottarelli, Giovanni Cherubin, Carsten Maple, Samuel N. Cohen, and Adrian Weller · 2022
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