3 papers
cs.DS2025
Breaking the Additive Error Barrier for Private and Efficient Graph Sparsification via Private Expander Decomposition
Anders Aamand, Justin Y. Chen, Mina Dalirrooyfard +4
We study differentially private algorithms for graph cut sparsification, a fundamental problem in algorithms, privacy, and machine learning. While significant progress has been mad…
cs.DS2025
SPARSE-PIVOT: Dynamic correlation clustering for node insertions
Mina Dalirrooyfard, Konstantin Makarychev, Slobodan Mitrović
We present a new Correlation Clustering algorithm for a dynamic setting where nodes are added one at a time. In this model, proposed by Cohen-Addad, Lattanzi, Maggiori, and Parotsi…
cs.LG2025
Privacy Amplification by Structured Subsampling for Deep Differentially Private Time Series Forecasting
Jan Schuchardt, Mina Dalirrooyfard, Jed Guzelkabaagac +3
Many forms of sensitive data, such as web traffic, mobility data, or hospital occupancy, are inherently sequential. The standard method for training machine learning models while e…