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Multi-Epoch Matrix Factorization Mechanisms for Private Machine Learning.

C. Choquette-Choo, H. McMahan, K. Rush, and A. Thakurta. CoRR, (2022)

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Differentially Private Matrix Completion, Revisited.P. Jain, O. Thakkar, and A. Thakurta. CoRR, (2017)Private Convex Optimization for Empirical Risk Minimization with Applications to High-dimensional Regression.D. Kifer, A. Smith, and A. Thakurta. COLT, volume 23 of JMLR Proceedings, page 25.1-25.40. JMLR.org, (2012)Private Empirical Risk Minimization Beyond the Worst Case: The Effect of the Constraint Set Geometry.K. Talwar, A. Thakurta, and L. Zhang. CoRR, (2014)Toward Training at ImageNet Scale with Differential Privacy.A. Kurakin, S. Chien, S. Song, R. Geambasu, A. Terzis, and A. Thakurta. CoRR, (2022)Differentially Private Image Classification from Features.H. Mehta, W. Krichene, A. Thakurta, A. Kurakin, and A. Cutkosky. CoRR, (2022)Private Gradient Descent for Linear Regression: Tighter Error Bounds and Instance-Specific Uncertainty Estimation.G. Brown, K. Dvijotham, G. Evans, D. Liu, A. Smith, and A. Thakurta. CoRR, (2024)Faster Differentially Private Convex Optimization via Second-Order Methods.A. Ganesh, M. Haghifam, T. Steinke, and A. Thakurta. CoRR, (2023)Differentially Private Stream Processing at Scale.B. Zhang, V. Doroshenko, P. Kairouz, T. Steinke, A. Thakurta, Z. Ma, H. Apte, and J. Spacek. CoRR, (2023)Amplification by Shuffling: From Local to Central Differential Privacy via Anonymity.Ú. Erlingsson, V. Feldman, I. Mironov, A. Raghunathan, K. Talwar, and A. Thakurta. SODA, page 2468-2479. SIAM, (2019)Privacy Amplification via Random Check-Ins.B. Balle, P. Kairouz, B. McMahan, O. Thakkar, and A. Thakurta. NeurIPS, (2020)

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Multi-Epoch Matrix Factorization Mechanisms for Private Machine Learning.

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Disambiguation of "Thakurta, Abhradeep"

copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Multi-Epoch Matrix Factorization Mechanisms for Private Machine Learning.

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Multi-Epoch Matrix Factorization Mechanisms for Private Machine Learning.