Certified Robustness to Adversarial Examples with Differential Privacy von Lecuyer, Mathias, Atlidakis, Vaggelis, Geambasu, Roxana, Hsu, Daniel, Jana, Suman

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Dispatch: Secure, Resilient Mobile Reporting von BISCUITWALA, Kanak, BULT, Willem, LECUYER, Mathias, PURTELL, T. J, ROSS, Madeline K. B, CHAINTREAU, Augustin, HASEMAN, Chris, LAM, Monica S, MCGREGOR, Susan E

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Pyramid: Enhancing Selectivity in Big Data Protection with Count Featurization von Lecuyer, Mathias, Spahn, Riley, Geambasu, Roxana, Tzu-Kuo Huang, Sen, Siddhartha

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Privacy Accounting and Quality Control in the Sage Differentially Private ML Platform von Lècuyer, Mathias, Spahn, Riley, Vodrahalli, Kiran, Geambasu, Roxana, Hsu, Daniel

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Fast Optimization of Weighted Sparse Decision Trees for use in Optimal Treatment Regimes and Optimal Policy Design von Behrouz, Ali, Lecuyer, Mathias, Rudin, Cynthia, Seltzer, Margo

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Measuring the Effect of Training Data on Deep Learning Predictions via Randomized Experiments von Lin, Jinkun, Zhang, Anqi, Lecuyer, Mathias, Li, Jinyang, Panda, Aurojit, Sen, Siddhartha

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Practical Privacy Filters and Odometers with R\'enyi Differential Privacy and Applications to Differentially Private Deep Learning von Lécuyer, Mathias

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DP-Adam: Correcting DP Bias in Adam's Second Moment Estimation von Tang, Qiaoyue, Lécuyer, Mathias

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Fast Optimization of Weighted Sparse Decision Trees for use in Optimal Treatment Regimes and Optimal Policy Design von Ali, Behrouz, Lecuyer, Mathias, Rudin, Cynthia, Seltzer, Margo

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Training and Evaluating Causal Forecasting Models for Time-Series von Crasson, Thomas, Nabet, Yacine, Lécuyer, Mathias

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Certified Robustness to Adversarial Examples with Differential Privacy von Lecuyer, Mathias, Atlidakis, Vaggelis, Geambasu, Roxana, Hsu, Daniel, Jana, Suman

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Privacy Accounting and Quality Control in the Sage Differentially Private ML Platform von Lecuyer, Mathias, Riley Spahn, Vodrahalli, Kiran, Geambasu, Roxana, Hsu, Daniel

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DP-AdamBC: Your DP-Adam Is Actually DP-SGD (Unless You Apply Bias Correction) von Tang, Qiaoyue, Shpilevskiy, Frederick, Lécuyer, Mathias

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PRIVACY FILTERS AND ODOMETERS FOR DEEP LEARNING von Lécuyer, Mathias François Roger

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Privacy filters and odometers for deep learning von Lécuyer, Mathias François Roger

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Practical Privacy Filters and Odometers with Rényi Differential Privacy and Applications to Differentially Private Deep Learning von Lécuyer, Mathias

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Measuring the Effect of Training Data on Deep Learning Predictions via Randomized Experiments von Lin, Jinkun, Zhang, Anqi, Lecuyer, Mathias, Li, Jinyang, Panda, Aurojit, Sen, Siddhartha

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Adaptive Randomized Smoothing: Certified Adversarial Robustness for Multi-Step Defences von Lyu, Saiyue, Shaikh, Shadab, Shpilevskiy, Frederick, Shelhamer, Evan, Lécuyer, Mathias

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DP-Adam: Correcting DP Bias in Adam's Second Moment Estimation von Tang, Qiaoyue, Lécuyer, Mathias

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Pyramid: Enhancing Selectivity in Big Data Protection with Count Featurization von Lecuyer, Mathias, Spahn, Riley, Geambasu, Roxana, Huang, Tzu-Kuo, Sen, Siddhartha

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