8 citations · 9 across the 6 of their papers we have counts for
7 papers
Soft Voting for Policy-Aware Private Data Synthesis
Yingge Hu, Gautham Ramesh Babu, Mostafa Milani
Blowfish privacy relaxes differential privacy (DP) by protecting only the attribute-value substitutions a data owner specifies as edges of a policy graph. A sparser policy can redu…
Structure Selection for Fairness-Constrained Differentially Private Data Synthesis
Naeim Ghahramanpour, Mostafa Milani
Differential privacy (DP) enables safe data release, with synthetic data generation emerging as a common approach in recent years. Yet standard synthesizers preserve all dependenci…
Beyond Accuracy: An Empirical Study of Uncertainty Estimation in Imputation
Zarin Tahia Hossain, Mostafa Milani
Handling missing data is a central challenge in data-driven analysis. Modern imputation methods not only aim for accurate reconstruction but also differ in how they represent and q…
Reducing Biases in Record Matching Through Scores Calibration
Mohammad Hossein Moslemi, Mostafa Milani
Record matching models typically output a real-valued matching score that is later consumed through thresholding, ranking, or human review. While fairness in record matching has mo…
Evaluating Blocking Biases in Entity Matching
Mohammad Hossein Moslemi, Harini Balamurugan, Mostafa Milani
Entity Matching (EM) is crucial for identifying equivalent data entities across different sources, a task that becomes increasingly challenging with the growth and heterogeneity of…
Threshold-Independent Fair Matching through Score Calibration
Mohammad Hossein Moslemi, Mostafa Milani
Entity Matching (EM) is a critical task in numerous fields, such as healthcare, finance, and public administration, as it identifies records that refer to the same entity within or…
OTClean: Data Cleaning for Conditional Independence Violations using Optimal Transport
Alireza Pirhadi, Mohammad Hossein Moslemi, Alexander Cloninger +2
Ensuring Conditional Independence (CI) constraints is pivotal for the development of fair and trustworthy machine learning models. In this paper, we introduce \sys, a framework tha…