Atefeh Gilani — accepted AI-conference papers

2025

GeoClip: Geometry-Aware Clipping for Differentially Private SGD

Atefeh Gilani, Naima Tasnim, Lalitha Sankar, Oliver Kosut

NeurIPS 2025poster

Differentially private stochastic gradient descent (DP-SGD) is the most widely used method for training machine learning models with provable privacy guarantees. A key challenge in DP-SGD is setting the per-sample gradient clipping threshold, which significantly affects the trade-off between privacy…

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2025

Optimizing Noise Distributions for Differential Privacy

Atefeh Gilani, Juan Felipe Gomez, Shahab Asoodeh, Flavio Calmon, Oliver Kosut, Lalitha Sankar

ICML 2025poster

We propose a unified optimization framework for designing continuous and discrete noise distributions that ensure differential privacy (DP) by minimizing Rényi DP, a variant of DP, under a cost constraint. Rényi DP has the advantage that by considering different values of the Rényi parameter $\alpha…

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