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Three Variants of Differential Privacy: Lossless Conversion and Applications

Aug 14, 2020
Shahab Asoodeh, Jiachun Liao, Flavio P. Calmon, Oliver Kosut, Lalitha Sankar

* An extended version of our previous paper (arXiv:2001.05990) presented at ISIT'20 

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On the alpha-loss Landscape in the Logistic Model

Jun 22, 2020
Tyler Sypherd, Mario Diaz, Lalitha Sankar, Gautam Dasarathy

* 5 pages, appeared in ISIT 2020 

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A Better Bound Gives a Hundred Rounds: Enhanced Privacy Guarantees via $f$-Divergences

Jan 16, 2020
Shahab Asoodeh, Jiachun Liao, Flavio P. Calmon, Oliver Kosut, Lalitha Sankar

* Submitted for Publication 

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Theoretical Guarantees for Model Auditing with Finite Adversaries

Nov 08, 2019
Mario Diaz, Peter Kairouz, Jiachun Liao, Lalitha Sankar

* 18 pages, 1 figure 

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Learning Generative Adversarial RePresentations (GAP) under Fairness and Censoring Constraints

Sep 27, 2019
Jiachun Liao, Chong Huang, Peter Kairouz, Lalitha Sankar

* 28 pages, 11 Figures. arXiv admin note: text overlap with arXiv:1807.05306 

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A Tunable Loss Function for Classification

Jun 26, 2019
Tyler Sypherd, Mario Diaz, Harshit Laddha, Lalitha Sankar, Peter Kairouz, Gautam Dasarathy

* Corrected email address 

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A Tunable Loss Function for Binary Classification

Mar 19, 2019
Tyler Sypherd, Mario Diaz, Lalitha Sankar, Peter Kairouz

* 9 pages, 1 figure, ISIT 2019 

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Generative Adversarial Privacy

Jul 13, 2018
Chong Huang, Peter Kairouz, Xiao Chen, Lalitha Sankar, Ram Rajagopal

* A preliminary version of this work was presented at the Privacy in Machine Learning and Artificial Intelligence Workshop, ICML 2018. arXiv admin note: text overlap with arXiv:1710.09549 

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Context-Aware Generative Adversarial Privacy

Dec 03, 2017
Chong Huang, Peter Kairouz, Xiao Chen, Lalitha Sankar, Ram Rajagopal

* Improved version of a paper accepted by Entropy Journal, Special Issue on Information Theory in Machine Learning and Data Science 

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