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researcher

Mostafa Milani

7 papers hereh-index 334 citations9 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author1
  • last author5

Across the 6 of 7 papers where every author was matched, so the position is known.

fields
  • cs.LG4
  • cs.DB2
  • cs.CR1
same name
  • Mostafa Milani — 11 papers, h 10
  • Mostafa Milani — 3 papers, h 2
  • Mostafa Milani — 2 papers, h 1
  • Mostafa Milani — 1 paper, h 0

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20242026
most citedThreshold-Independent Fair Matching through Score Calibration

8 citations · 9 across the 6 of their papers we have counts for

collaborators
Showing cs.LGShow all

4 papers · 1 filter

cs.LG2024

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…

cs.LG2024

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…

cs.LG2024★ 8 cited

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…

cs.LG2024

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…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.