Shiva Kasiviswanathan
Shiva Kasiviswanathan
Amazon Machine Learning
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What can we learn privately?
SP Kasiviswanathan, HK Lee, K Nissim, S Raskhodnikova, A Smith
SIAM Journal on Computing 40 (3), 793-826, 2011
Composition attacks and auxiliary information in data privacy
SR Ganta, SP Kasiviswanathan, A Smith
Proceedings of the 14th ACM SIGKDD international conference on Knowledge …, 2008
Analyzing graphs with node differential privacy
SP Kasiviswanathan, K Nissim, S Raskhodnikova, A Smith
Theory of Cryptography Conference, 457-476, 2013
Simple Black-Box Adversarial Attacks on Deep Neural Networks.
N Narodytska, SP Kasiviswanathan
CVPR Workshops 2, 2017
Simple black-box adversarial perturbations for deep networks
N Narodytska, SP Kasiviswanathan
arXiv preprint arXiv:1612.06299, 2016
Emerging topic detection using dictionary learning
SP Kasiviswanathan, P Melville, A Banerjee, V Sindhwani
Proceedings of the 20th ACM international conference on Information and …, 2011
Verifying properties of binarized deep neural networks
N Narodytska, S Kasiviswanathan, L Ryzhyk, M Sagiv, T Walsh
Proceedings of the AAAI Conference on Artificial Intelligence 32 (1), 2018
Subsampled rényi differential privacy and analytical moments accountant
YX Wang, B Balle, SP Kasiviswanathan
The 22nd International Conference on Artificial Intelligence and Statistics …, 2019
On the'semantics' of differential privacy: A bayesian formulation
SP Kasiviswanathan, A Smith
Journal of Privacy and Confidentiality 6 (1), 2014
The price of privately releasing contingency tables and the spectra of random matrices with correlated rows
SP Kasiviswanathan, M Rudelson, A Smith, J Ullman
Proceedings of the forty-second ACM symposium on Theory of computing, 775-784, 2010
Bounds on the sample complexity for private learning and private data release
A Beimel, SP Kasiviswanathan, K Nissim
Theory of Cryptography Conference, 437-454, 2010
Private spatial data aggregation in the local setting
R Chen, H Li, AK Qin, SP Kasiviswanathan, H Jin
2016 IEEE 32nd International Conference on Data Engineering (ICDE), 289-300, 2016
A note on differential privacy: Defining resistance to arbitrary side information
SP Kasiviswanathan, A Smith
CoRR abs/0803.3946, 2008
Bounds on the sample complexity for private learning and private data release
A Beimel, H Brenner, SP Kasiviswanathan, K Nissim
Machine learning 94 (3), 401-437, 2014
Algorithms for Counting 2-Sat Solutions and Colorings with Applications
M Fürer, SP Kasiviswanathan
International Conference on Algorithmic Applications in Management, 47-57, 2007
Efficient and practical stochastic subgradient descent for nuclear norm regularization
H Avron, S Kale, S Kasiviswanathan, V Sindhwani
arXiv preprint arXiv:1206.6384, 2012
Online L1-Dictionary Learning with Application to Novel Document Detection.
SP Kasiviswanathan, H Wang, A Banerjee, P Melville
NIPS, 2267-2275, 2012
Online dictionary learning on symmetric positive definite manifolds with vision applications
S Zhang, S Kasiviswanathan, PC Yuen, M Harandi
Twenty-Ninth AAAI Conference on Artificial Intelligence, 2015
Streaming anomaly detection using randomized matrix sketching
H Huang, SP Kasiviswanathan
Proceedings of the VLDB Endowment 9 (3), 192-203, 2015
Efficient private empirical risk minimization for high-dimensional learning
SP Kasiviswanathan, H Jin
International Conference on Machine Learning, 488-497, 2016
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