Kevin Aydin — accepted AI-conference papers

2025

Nearly-Linear Time and Massively Parallel Algorithms for $k$-anonymity

Kevin Aydin, Honghao Lin, David Woodruff, Peilin Zhong

NeurIPS 2025poster

$k$-anonymity is a widely-used privacy-preserving concept that ensures each record in a dataset is indistinguishable from at least $k-1$ other records. In this paper, we revisit $k$-anonymity by suppression and give an $O(k)$-approximation algorithm with a nearly-linear runtime of $\tilde{O}(nd + n^…

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2019

Variance Reduction in Bipartite Experiments through Correlation Clustering

Jean Pouget-Abadie, Kevin Aydin, Warren Schudy, Kay Brodersen, Vahab Mirrokni

NeurIPS 2019poster

Causal inference in randomized experiments typically assumes that the units of randomization and the units of analysis are one and the same. In some applications, however, these two roles are played by distinct entities linked by a bipartite graph. The key challenge in such bipartite settings is how…

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