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Author search results
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- Abhradeep Thakurta
aka: Abhradeep Guha Thakurta
Microsoft Research Silicon Valley
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Publication search results
found 112 matches
- 2024
- Gavin Brown, Krishnamurthy Dvijotham, Georgina Evans, Daogao Liu, Adam Smith, Abhradeep Thakurta:
Private Gradient Descent for Linear Regression: Tighter Error Bounds and Instance-Specific Uncertainty Estimation. CoRR abs/2402.13531 (2024) - 2023
- Natalia Ponomareva, Hussein Hazimeh, Alex Kurakin, Zheng Xu, Carson Denison, H. Brendan McMahan, Sergei Vassilvitskii, Steve Chien, Abhradeep Guha Thakurta:
How to DP-fy ML: A Practical Guide to Machine Learning with Differential Privacy. J. Artif. Intell. Res. 77: 1113-1201 (2023) - Harsh Mehta, Walid Krichene, Abhradeep Guha Thakurta, Alexey Kurakin, Ashok Cutkosky:
Differentially Private Image Classification from Features. Trans. Mach. Learn. Res. 2023 (2023) - Harsh Mehta, Abhradeep Guha Thakurta, Alexey Kurakin, Ashok Cutkosky:
Towards Large Scale Transfer Learning for Differentially Private Image Classification. Trans. Mach. Learn. Res. 2023 (2023) - Naman Agarwal, Satyen Kale, Karan Singh, Abhradeep Thakurta:
Differentially Private and Lazy Online Convex Optimization. COLT 2023: 4599-4632 - Arun Ganesh, Abhradeep Thakurta, Jalaj Upadhyay:
Universality of Langevin Diffusion for Private Optimization, with Applications to Sampling from Rashomon Sets. COLT 2023: 1730-1773 - Matthew Jagielski, Om Thakkar, Florian Tramèr, Daphne Ippolito, Katherine Lee, Nicholas Carlini, Eric Wallace, Shuang Song, Abhradeep Guha Thakurta, Nicolas Papernot, Chiyuan Zhang:
Measuring Forgetting of Memorized Training Examples. ICLR 2023 - Christopher A. Choquette-Choo, Hugh Brendan McMahan, J. Keith Rush, Abhradeep Guha Thakurta:
Multi-Epoch Matrix Factorization Mechanisms for Private Machine Learning. ICML 2023: 5924-5963 - Arun Ganesh, Mahdi Haghifam, Milad Nasr, Sewoong Oh, Thomas Steinke, Om Thakkar, Abhradeep Guha Thakurta, Lun Wang:
Why Is Public Pretraining Necessary for Private Model Training? ICML 2023: 10611-10627 - Walid Krichene, Prateek Jain, Shuang Song, Mukund Sundararajan, Abhradeep Guha Thakurta, Li Zhang:
Multi-Task Differential Privacy Under Distribution Skew. ICML 2023: 17784-17807 - Christopher A. Choquette-Choo, Arun Ganesh, Ryan McKenna, H. Brendan McMahan, John Rush, Abhradeep Guha Thakurta, Zheng Xu:
(Amplified) Banded Matrix Factorization: A unified approach to private training. NeurIPS 2023 - Arun Ganesh, Mahdi Haghifam, Thomas Steinke, Abhradeep Guha Thakurta:
Faster Differentially Private Convex Optimization via Second-Order Methods. NeurIPS 2023 - Daogao Liu, Arun Ganesh, Sewoong Oh, Abhradeep Guha Thakurta:
Private (Stochastic) Non-Convex Optimization Revisited: Second-Order Stationary Points and Excess Risks. NeurIPS 2023 - Stephan Rabanser, Anvith Thudi, Abhradeep Guha Thakurta, Krishnamurthy Dvijotham, Nicolas Papernot:
Training Private Models That Know What They Don't Know. NeurIPS 2023 - Walid Krichene, Prateek Jain, Shuang Song, Mukund Sundararajan, Abhradeep Thakurta, Li Zhang:
Multi-Task Differential Privacy Under Distribution Skew. CoRR abs/2302.07975 (2023) - Arun Ganesh, Mahdi Haghifam, Milad Nasr, Sewoong Oh, Thomas Steinke, Om Thakkar, Abhradeep Thakurta, Lun Wang:
Why Is Public Pretraining Necessary for Private Model Training? CoRR abs/2302.09483 (2023) - Arun Ganesh, Daogao Liu, Sewoong Oh, Abhradeep Thakurta:
Private (Stochastic) Non-Convex Optimization Revisited: Second-Order Stationary Points and Excess Risks. CoRR abs/2302.09699 (2023) - Natalia Ponomareva, Hussein Hazimeh, Alex Kurakin, Zheng Xu, Carson Denison, H. Brendan McMahan, Sergei Vassilvitskii, Steve Chien, Abhradeep Thakurta:
How to DP-fy ML: A Practical Guide to Machine Learning with Differential Privacy. CoRR abs/2303.00654 (2023) - Bing Zhang, Vadym Doroshenko, Peter Kairouz, Thomas Steinke, Abhradeep Thakurta, Ziyin Ma, Himani Apte, Jodi Spacek:
Differentially Private Stream Processing at Scale. CoRR abs/2303.18086 (2023) - Rachel Cummings, Damien Desfontaines, David Evans, Roxana Geambasu, Matthew Jagielski, Yangsibo Huang, Peter Kairouz, Gautam Kamath, Sewoong Oh, Olga Ohrimenko, Nicolas Papernot, Ryan Rogers, Milan Shen, Shuang Song, Weijie J. Su, Andreas Terzis, Abhradeep Thakurta, Sergei Vassilvitskii, Yu-Xiang Wang, Li Xiong, Sergey Yekhanin, Da Yu, Huanyu Zhang, Wanrong Zhang:
Challenges towards the Next Frontier in Privacy. CoRR abs/2304.06929 (2023) - Arun Ganesh, Mahdi Haghifam, Thomas Steinke, Abhradeep Thakurta:
Faster Differentially Private Convex Optimization via Second-Order Methods. CoRR abs/2305.13209 (2023) - Stephan Rabanser, Anvith Thudi, Abhradeep Thakurta, Krishnamurthy Dvijotham, Nicolas Papernot:
Training Private Models That Know What They Don't Know. CoRR abs/2305.18393 (2023) - Christopher A. Choquette-Choo, Arun Ganesh, Ryan McKenna, H. Brendan McMahan, Keith Rush, Abhradeep Guha Thakurta, Zheng Xu:
(Amplified) Banded Matrix Factorization: A unified approach to private training. CoRR abs/2306.08153 (2023) - Christopher A. Choquette-Choo, Krishnamurthy Dvijotham, Krishna Pillutla, Arun Ganesh, Thomas Steinke, Abhradeep Thakurta:
Correlated Noise Provably Beats Independent Noise for Differentially Private Learning. CoRR abs/2310.06771 (2023) - Walid Krichene, Nicolas Mayoraz, Steffen Rendle, Shuang Song, Abhradeep Thakurta, Li Zhang:
Private Learning with Public Features. CoRR abs/2310.15454 (2023) - Christopher A. Choquette-Choo, Arun Ganesh, Thomas Steinke, Abhradeep Thakurta:
Privacy Amplification for Matrix Mechanisms. CoRR abs/2310.15526 (2023) - Naman Agarwal, Satyen Kale, Karan Singh, Abhradeep Guha Thakurta:
Improved Differentially Private and Lazy Online Convex Optimization. CoRR abs/2312.11534 (2023) - 2022
- Oren Mangoubi, Yikai Wu, Satyen Kale, Abhradeep Thakurta, Nisheeth K. Vishnoi:
Private Matrix Approximation and Geometry of Unitary Orbits. COLT 2022: 3547-3588 - Prateek Varshney, Abhradeep Thakurta, Prateek Jain:
(Nearly) Optimal Private Linear Regression for Sub-Gaussian Data via Adaptive Clipping. COLT 2022: 1126-1166 - Ehsan Amid, Arun Ganesh, Rajiv Mathews, Swaroop Ramaswamy, Shuang Song, Thomas Steinke, Vinith M. Suriyakumar, Om Thakkar, Abhradeep Thakurta:
Public Data-Assisted Mirror Descent for Private Model Training. ICML 2022: 517-535
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