Shiva Kasiviswanathan
Shiva Kasiviswanathan
Amazon Machine Learning
Verified email at amazon.com - Homepage
Title
Cited by
Cited by
Year
What can we learn privately?
SP Kasiviswanathan, HK Lee, K Nissim, S Raskhodnikova, A Smith
SIAM Journal on Computing 40 (3), 793-826, 2011
6682011
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
3842008
Analyzing graphs with node differential privacy
SP Kasiviswanathan, K Nissim, S Raskhodnikova, A Smith
Theory of Cryptography Conference, 457-476, 2013
2092013
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
1522011
Simple black-box adversarial perturbations for deep networks
N Narodytska, SP Kasiviswanathan
arXiv preprint arXiv:1612.06299, 2016
1402016
Simple black-box adversarial attacks on deep neural networks
N Narodytska, S Kasiviswanathan
2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops …, 2017
1312017
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
1042018
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
1012010
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
862010
A note on differential privacy: Defining resistance to arbitrary side information
SP Kasiviswanathan, A Smith
CoRR abs/0803.3946, 2008
832008
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
812016
On the'semantics' of differential privacy: A bayesian formulation
SP Kasiviswanathan, A Smith
Journal of Privacy and Confidentiality 6 (1), 2014
782014
Subsampled Rényi differential privacy and analytical moments accountant
YX Wang, B Balle, SP Kasiviswanathan
The 22nd International Conference on Artificial Intelligence and Statistics …, 2019
712019
Algorithms for Counting 2-Sat Solutions and Colorings with Applications
M Fürer, SP Kasiviswanathan
International Conference on Algorithmic Applications in Management, 47-57, 2007
682007
Efficient and practical stochastic subgradient descent for nuclear norm regularization
H Avron, S Kale, S Kasiviswanathan, V Sindhwani
arXiv preprint arXiv:1206.6384, 2012
632012
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
592014
Online l1-dictionary learning with application to novel document detection
S Kasiviswanathan, H Wang, A Banerjee, P Melville
Advances in Neural Information Processing Systems 25, 2258-2266, 2012
582012
Online dictionary learning on symmetric positive definite manifolds with vision applications
S Zhang, S Kasiviswanathan, P Yuen, M Harandi
Proceedings of the AAAI Conference on Artificial Intelligence 29 (1), 2015
502015
Streaming anomaly detection using randomized matrix sketching
H Huang, SP Kasiviswanathan
Proceedings of the VLDB Endowment 9 (3), 192-203, 2015
402015
Efficient private empirical risk minimization for high-dimensional learning
SP Kasiviswanathan, H Jin
International Conference on Machine Learning, 488-497, 2016
352016
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