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cs.LG2024
Noise is All You Need: Private Second-Order Convergence of Noisy SGD
Dmitrii Avdiukhin, Michael Dinitz, Chenglin Fan +1
Private optimization is a topic of major interest in machine learning, with differentially private stochastic gradient descent (DP-SGD) playing a key role in both theory and practi…
cs.CR2024
Differentially Private Algorithms for Graph Cuts: A Shifting Mechanism Approach and More
Rishi Chandra, Michael Dinitz, Chenglin Fan +1
In this paper, we address the challenge of differential privacy in the context of graph cuts, specifically focusing on the multiway cut and the minimum -cut. We introduce edge-d…