Artikel dengan mandat akses publik - Abhradeep Guha ThakurtaPelajari lebih lanjut
Tersedia di suatu tempat: 19
Practical locally private heavy hitters
R Bassily, K Nissim, U Stemmer, A Guha Thakurta
Advances in Neural Information Processing Systems 30, 2017
Mandat: US National Science Foundation
Private stochastic convex optimization with optimal rates
R Bassily, V Feldman, K Talwar, A Guha Thakurta
Advances in neural information processing systems 32, 2019
Mandat: US National Science Foundation
Towards practical differentially private convex optimization
R Iyengar, JP Near, D Song, O Thakkar, A Thakurta, L Wang
2019 IEEE symposium on security and privacy (SP), 299-316, 2019
Mandat: US National Science Foundation, US Department of Defense
Is interaction necessary for distributed private learning?
A Smith, A Thakurta, J Upadhyay
2017 IEEE Symposium on Security and Privacy (SP), 58-77, 2017
Mandat: US National Science Foundation
Is private learning possible with instance encoding?
N Carlini, S Deng, S Garg, S Jha, S Mahloujifar, M Mahmoody, A Thakurta, ...
2021 IEEE Symposium on Security and Privacy (SP), 410-427, 2021
Mandat: US National Science Foundation, US Department of Defense
Model-agnostic private learning
R Bassily, O Thakkar, A Guha Thakurta
Advances in neural information processing systems 31, 2018
Mandat: US National Science Foundation
Improved differential privacy for sgd via optimal private linear operators on adaptive streams
S Denisov, HB McMahan, J Rush, A Smith, A Guha Thakurta
Advances in Neural Information Processing Systems 35, 5910-5924, 2022
Mandat: US National Science Foundation
Differentially private matrix completion revisited
P Jain, OD Thakkar, A Thakurta
International Conference on Machine Learning, 2215-2224, 2018
Mandat: US National Science Foundation
The flajolet-martin sketch itself preserves differential privacy: Private counting with minimal space
A Smith, S Song, A Guha Thakurta
Advances in Neural Information Processing Systems 33, 19561-19572, 2020
Mandat: US National Science Foundation
Differentially private model personalization
P Jain, J Rush, A Smith, S Song, A Guha Thakurta
Advances in Neural Information Processing Systems 34, 29723-29735, 2021
Mandat: US National Science Foundation
Testing the Lipschitz property over product distributions with applications to data privacy
K Dixit, M Jha, S Raskhodnikova, A Thakurta
Theory of Cryptography Conference, 418-436, 2013
Mandat: US National Institutes of Health
Erasure-resilient property testing
K Dixit, S Raskhodnikova, A Thakurta, N Varma
SIAM Journal on Computing 47 (2), 295-329, 2018
Mandat: US National Science Foundation
Universality of langevin diffusion for private optimization, with applications to sampling from rashomon sets
A Ganesh, A Thakurta, J Upadhyay
The Thirty Sixth Annual Conference on Learning Theory, 1730-1773, 2023
Mandat: US National Science Foundation
Differentially private survey research
G Evans, G King, AD Smith, A Thakurta
American Journal of Political Science, 2022
Mandat: US National Science Foundation
Training private models that know what they don’t know
S Rabanser, A Thudi, A Guha Thakurta, K Dvijotham, N Papernot
Advances in Neural Information Processing Systems 36, 53711-53727, 2023
Mandat: US Department of Defense, Natural Sciences and Engineering Research Council …
Private matrix approximation and geometry of unitary orbits
O Mangoubi, Y Wu, S Kale, A Thakurta, NK Vishnoi
Conference on Learning Theory, 3547-3588, 2022
Mandat: US National Science Foundation
A separation result between data-oblivious and data-aware poisoning attacks
S Deng, S Garg, S Jha, S Mahloujifar, M Mahmoody, A Guha Thakurta
Advances in Neural Information Processing Systems 34, 10862-10875, 2021
Mandat: US National Science Foundation, US Department of Defense
Differentially private model personalization
A Smith, P Jain, K Rush, S Song, AG Thakurta
Mandat: US National Science Foundation
Differentially Private Survey Research: Supplementary (Online) Appendices
G Evans, G King, AD Smith, A Thakurta
Mandat: US National Science Foundation
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