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Private Learning with Public Features.

W. Krichene, N. Mayoraz, S. Rendle, S. Song, A. Thakurta, and L. Zhang. AISTATS, volume 238 of Proceedings of Machine Learning Research, page 4150-4158. PMLR, (2024)

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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)Differentially Private Matrix Completion, Revisited.P. Jain, O. Thakkar, and A. Thakurta. CoRR, (2017)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)Differentially Private Image Classification from Features.H. Mehta, W. Krichene, A. Thakurta, A. Kurakin, and A. Cutkosky. CoRR, (2022)Toward Training at ImageNet Scale with Differential Privacy.A. Kurakin, S. Chien, S. Song, R. Geambasu, A. Terzis, and A. Thakurta. CoRR, (2022)Private Empirical Risk Minimization Beyond the Worst Case: The Effect of the Constraint Set Geometry.K. Talwar, A. Thakurta, and L. Zhang. CoRR, (2014)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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Disambiguation of "Thakurta, Abhradeep"

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Private Learning with Public Features.

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Disambiguation of "Thakurta, Abhradeep"

copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Private Learning with Public Features.

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Private Learning with Public Features.