Fetching the paper…
Reading the bibliography…
Mechanisms used in privacy-preserving machine learning often aim to guarantee differential privacy (DP) during model training.
Exponentially many local minima for single neurons
Peter Auer, Mark Herbster, and Manfred KK Warmuth. 1995 · 1995
Earlier work this paper cites.
Statistical modeling: The two cultures (with comments and a rejoinder by the author)
Leo Breiman. 2001 · 2001
Earlier work this paper cites.
Calibrating noise to sensitivity in private data analysis. In Theory of cryptography conference . Springer
Cynthia Dwork, Frank McSherry, Kobbi Nissim, and Adam Smith. 2006 · 2006
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
Earlier work this paper cites.
Differentially private empirical risk minimization
Kamalika Chaudhuri, Claire Monteleoni, and Anand D Sarwate. 2011 · 2011
Earlier work this paper cites.
Fairness through awareness. In Proceedings of the 3rd innovations in theoretical computer science conference
Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Richard Zemel. 2012 · 2012
Earlier work this paper cites.
Learning in a Large Function Space: Privacy-Preserving Mechanisms for SVM Learning
Benjamin IP Rubinstein, Peter L Bartlett, Ling Huang, and Nina Taft. 2012 · 2012
Earlier work this paper cites.
The scored society: Due process for automated predictions
Danielle Keats Citron and Frank Pasquale. 2014 · 2014
Earlier work this paper cites.
The algorithmic foundations of differential privacy
Cynthia Dwork, Aaron Roth, et al · 2014
Earlier work this paper cites.
Deep roto-translation scattering for object classification. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition
Edouard Oyallon and Stéphane Mallat. 2015 · 2015
Earlier work this paper cites.
Deep learning with differential privacy. In Proceedings of the ACM SIGSAC conference on computer and communications security
Martin Abadi, Andy Chu, Ian Goodfellow, H Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang. 2016 · 2016
Earlier work this paper cites.
UCI Machine Learning Repository
Dheeru Dua and Casey Graff. 2017 · 2017
Earlier work this paper cites.
Bolt-on differential privacy for scalable stochastic gradient descent-based analytics. In Proceedings of the 2017 ACM International Conference on Management of Data
Xi Wu, Fengan Li, Arun Kumar, Kamalika Chaudhuri, Somesh Jha, and Jeffrey Naughton. 2017 · 2017
Earlier work this paper cites.
Differential privacy: A primer for a non-technical audience
Alexandra Wood, Micah Altman, Aaron Bembenek, Mark Bun, Marco Gaboardi, James Honaker, Kobbi Nissim, David R O’Brien, Thomas Steinke, and Salil Vadhan. 2018 · 2018
Cited alongside, same era.
Differential privacy has disparate impact on model accuracy
Eugene Bagdasaryan, Omid Poursaeed, and Vitaly Shmatikov. 2019 · 2019
Cited alongside, same era.
On the compatibility of privacy and fairness. In Adjunct Publication of the 27th Conference on User Modeling, Adaptation and Personalization
Rachel Cummings, Varun Gupta, Dhamma Kimpara, and Jamie Morgenstern. 2019 · 2019
Cited alongside, same era.
All Models are Wrong, but Many are Useful: Learning a Variable’s Importance by Studying an Entire Class of Prediction Models Simultaneously
Aaron Fisher, Cynthia Rudin, and Francesca Dominici. 2019 · 2019
Cited alongside, same era.
Diffprivlib: the IBM differential privacy library
Naoise Holohan, Stefano Braghin, Pól Mac Aonghusa, and Killian Levacher. 2019 · 2019
Characterizing fairness over the set of good models under selective labels. In International Conference on Machine Learning . PMLR
Amanda Coston, Ashesh Rambachan, and Alexandra Chouldechova. 2021 · 2021
Later among the works it cites.
Differentially Private Learning Needs Better Features (or Much More Data). In International Conference on Learning Representations
Florian Tramer and Dan Boneh. 2021 · 2021
Later among the works it cites.
seaborn: statistical data visualization
Michael L. Waskom. 2021 · 2021
Later among the works it cites.
Opacus: User-Friendly Differential Privacy Library in PyTorch
Ashkan Yousefpour, Igor Shilov, Alexandre Sablayrolles, Davide Testuggine, Karthik Prasad, Mani Malek, John Nguyen, Sayan Ghosh, Akash Bharadwaj, Jessica Zhao, Graham Cormode, and Ilya Mironov. 2021 · 2021
Later among the works it cites.
Model Multiplicity: Opportunities, Concerns, and Solutions. In ACM Conference on Fairness, Accountability, and Transparency (FAccT)
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Evaluating differentially private machine learning in practice. In USENIX Security Symposium
Bargav Jayaraman and David Evans. 2019 · 2019
Cited alongside, same era.
Pytorch: An imperative style, high-performance deep learning library. In Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems (NeurIPS)
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al · 2019
Cited alongside, same era.
Underspecification presents challenges for credibility in modern machine learning
Alexander D’Amour, Katherine Heller, Dan Moldovan, Ben Adlam, Babak Alipanahi, Alex Beutel, Christina Chen, Jonathan Deaton, Jacob Eisenstein, Matthew D Hoffman, et al · 2020
Cited alongside, same era.
Array programming with NumPy
Charles R. Harris, K. Jarrod Millman, Stéfan J. van der Walt, Ralf Gommers, Pauli Virtanen, David Cournapeau, Eric Wieser, Julian Taylor, Sebastian Berg, Nathaniel J. Smith, Robert Kern, Matti Picus, Stephan Hoyer, Marten H. van Kerkwijk, Matthew Brett, Allan Haldane, Jaime Fernández del Río, Mark Wiebe, Pearu Peterson, Pierre Gérard-Marchant, Kevin Sheppard, Tyler Reddy, Warren Weckesser, Hameer Abbasi, Christoph Gohlke, and Travis E. Oliphant. 2020 · 2020
Cited alongside, same era.
Predictive multiplicity in classification. In International Conference on Machine Learning . PMLR
Charles Marx, Flavio Calmon, and Berk Ustun. 2020 · 2020
Cited alongside, same era.
pandas-dev/pandas: Pandas
The pandas development team. 2020 · 2020
Cited alongside, same era.
SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python
Pauli Virtanen, Ralf Gommers, Travis E. Oliphant, Matt Haberland, Tyler Reddy, David Cournapeau, Evgeni Burovski, Pearu Peterson, Warren Weckesser, Jonathan Bright, Stéfan J. van der Walt, Matthew Brett, Joshua Wilson, K. Jarrod Millman, Nikolay Mayorov, Andrew R. J. Nelson, Eric Jones, Robert Kern, Eric Larson, C J Carey, İlhan Polat, Yu Feng, Eric W. Moore, Jake VanderPlas, Denis Laxalde, Josef Perktold, Robert Cimrman, Ian Henriksen, E. A. Quintero, Charles R. Harris, Anne M. Archibald, Antônio H. Ribeiro, Fabian Pedregosa, Paul van Mulbregt, and SciPy 1.0 Contributors. 2020 · 2020
Cited alongside, same era.
Emily Black, Manish Raghavan, and Solon Barocas. 2022 · 2022
Later among the works it cites.
The Algorithmic Leviathan: Arbitrariness, Fairness, and Opportunity in Algorithmic Decision-Making Systems
Kathleen Creel and Deborah Hellman. 2022 · 2022
Later among the works it cites.
Robin Hood and Matthew Effects: Differential Privacy Has Disparate Impact on Synthetic Data. In International Conference on Machine Learning . PMLR
Georgi Ganev, Bristena Oprisanu, and Emiliano De Cristofaro. 2022 · 2022
Later among the works it cites.
Rashomon Capacity: A Metric for Predictive Multiplicity in Probabilistic Classification
Hsiang Hsu and Flavio du Pin Calmon. 2022 · 2022
Later among the works it cites.
Hyperparameter tuning with Renyi differential privacy. In International Conference on Learning Representations
Nicolas Papernot and Thomas Steinke. 2022 · 2022
Later among the works it cites.
How unfair is private learning?. In Uncertainty in Artificial Intelligence . PMLR
Amartya Sanyal, Yaxi Hu, and Fanny Yang. 2022 · 2022
Later among the works it cites.
On the existence of simpler machine learning models. In ACM Conference on Fairness, Accountability, and Transparency (FAccT)
Lesia Semenova, Cynthia Rudin, and Ronald Parr. 2022 · 2022
Later among the works it cites.
Generalization Bounds for Noisy Iterative Algorithms Using Properties of Additive Noise Channels
Hao Wang, Rui Gao, and Flavio P Calmon. 2023 · 2023
Closest in time.
Predictive Multiplicity in Probabilistic Classification. In AAAI
Jamelle Watson-Daniels, David C Parkes, and Berk Ustun. 2023 · 2023
Closest in time.