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Private Stochastic Convex Optimization: Optimal Rates in $\ell_1$ Geometry


Mar 02, 2021
Hilal Asi, Vitaly Feldman, Tomer Koren, Kunal Talwar


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Lossless Compression of Efficient Private Local Randomizers


Feb 24, 2021
Vitaly Feldman, Kunal Talwar


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Hiding Among the Clones: A Simple and Nearly Optimal Analysis of Privacy Amplification by Shuffling


Dec 25, 2020
Vitaly Feldman, Audra McMillan, Kunal Talwar

* Minor revision 

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When is Memorization of Irrelevant Training Data Necessary for High-Accuracy Learning?


Dec 11, 2020
Gavin Brown, Mark Bun, Vitaly Feldman, Adam Smith, Kunal Talwar


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On the Error Resistance of Hinge Loss Minimization


Dec 02, 2020
Kunal Talwar


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Faster Differentially Private Samplers via Rényi Divergence Analysis of Discretized Langevin MCMC


Oct 27, 2020
Arun Ganesh, Kunal Talwar

* To appear in NeurIPS 2020 

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Stochastic Optimization with Laggard Data Pipelines


Oct 26, 2020
Naman Agarwal, Rohan Anil, Tomer Koren, Kunal Talwar, Cyril Zhang

* Published as a conference paper at NeurIPS 2020 

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Stability of Stochastic Gradient Descent on Nonsmooth Convex Losses


Jun 12, 2020
Raef Bassily, Vitaly Feldman, Cristóbal Guzmán, Kunal Talwar

* 32 pages 

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Private Stochastic Convex Optimization: Optimal Rates in Linear Time


May 10, 2020
Vitaly Feldman, Tomer Koren, Kunal Talwar


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Exploring the Memorization-Generalization Continuum in Deep Learning


Feb 08, 2020
Ziheng Jiang, Chiyuan Zhang, Kunal Talwar, Michael C. Mozer


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Computational Separations between Sampling and Optimization


Nov 05, 2019
Kunal Talwar

* NeurIPS 2019 

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Rényi Differential Privacy of the Sampled Gaussian Mechanism


Aug 28, 2019
Ilya Mironov, Kunal Talwar, Li Zhang

* 14 pages 

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Private Stochastic Convex Optimization with Optimal Rates


Aug 27, 2019
Raef Bassily, Vitaly Feldman, Kunal Talwar, Abhradeep Thakurta


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Semi-Cyclic Stochastic Gradient Descent


Apr 23, 2019
Hubert Eichner, Tomer Koren, H. Brendan McMahan, Nathan Srebro, Kunal Talwar


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Better Algorithms for Stochastic Bandits with Adversarial Corruptions


Mar 28, 2019
Anupam Gupta, Tomer Koren, Kunal Talwar


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Amplification by Shuffling: From Local to Central Differential Privacy via Anonymity


Nov 29, 2018
Úlfar Erlingsson, Vitaly Feldman, Ilya Mironov, Ananth Raghunathan, Kunal Talwar, Abhradeep Thakurta


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Private Selection from Private Candidates


Nov 19, 2018
Jingcheng Liu, Kunal Talwar

* 38 pages 

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Privacy Amplification by Iteration


Aug 20, 2018
Vitaly Feldman, Ilya Mironov, Kunal Talwar, Abhradeep Thakurta

* Extended abstract appears in Foundations of Computer Science (FOCS) 2018 

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Online Linear Quadratic Control


Jun 19, 2018
Alon Cohen, Avinatan Hassidim, Tomer Koren, Nevena Lazic, Yishay Mansour, Kunal Talwar


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Online learning over a finite action set with limited switching


Jun 13, 2018
Jason Altschuler, Kunal Talwar

* Extended abstract to appear in the proceedings of the 2018 Conference on Learning Theory (COLT) 

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Adversarially Robust Generalization Requires More Data


May 02, 2018
Ludwig Schmidt, Shibani Santurkar, Dimitris Tsipras, Kunal Talwar, Aleksander Mądry

* Small changes for biblatex compatibility 

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Scalable Private Learning with PATE


Feb 24, 2018
Nicolas Papernot, Shuang Song, Ilya Mironov, Ananth Raghunathan, Kunal Talwar, Úlfar Erlingsson

* Published as a conference paper at ICLR 2018 

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Learning Differentially Private Recurrent Language Models


Feb 24, 2018
H. Brendan McMahan, Daniel Ramage, Kunal Talwar, Li Zhang

* Camera-ready ICLR 2018 version, minor edits from previous 

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On the Protection of Private Information in Machine Learning Systems: Two Recent Approaches


Aug 26, 2017
Martín Abadi, Úlfar Erlingsson, Ian Goodfellow, H. Brendan McMahan, Ilya Mironov, Nicolas Papernot, Kunal Talwar, Li Zhang

* IEEE 30th Computer Security Foundations Symposium (CSF), pages 1--6, 2017 

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Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data


Mar 03, 2017
Nicolas Papernot, Martín Abadi, Úlfar Erlingsson, Ian Goodfellow, Kunal Talwar

* Accepted to ICLR 17 as an oral 

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Private Empirical Risk Minimization Beyond the Worst Case: The Effect of the Constraint Set Geometry


Nov 20, 2016
Kunal Talwar, Abhradeep Thakurta, Li Zhang


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Deep Learning with Differential Privacy


Oct 24, 2016
Martín Abadi, Andy Chu, Ian Goodfellow, H. Brendan McMahan, Ilya Mironov, Kunal Talwar, Li Zhang


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Sketching and Neural Networks


Apr 19, 2016
Amit Daniely, Nevena Lazic, Yoram Singer, Kunal Talwar


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TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems


Mar 16, 2016
Martín Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S. Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Ian Goodfellow, Andrew Harp, Geoffrey Irving, Michael Isard, Yangqing Jia, Rafal Jozefowicz, Lukasz Kaiser, Manjunath Kudlur, Josh Levenberg, Dan Mane, Rajat Monga, Sherry Moore, Derek Murray, Chris Olah, Mike Schuster, Jonathon Shlens, Benoit Steiner, Ilya Sutskever, Kunal Talwar, Paul Tucker, Vincent Vanhoucke, Vijay Vasudevan, Fernanda Viegas, Oriol Vinyals, Pete Warden, Martin Wattenberg, Martin Wicke, Yuan Yu, Xiaoqiang Zheng

* Version 2 updates only the metadata, to correct the formatting of Mart\'in Abadi's name 

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