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D. Oberski

4 papers hereh-index 161.6k citations50 works total

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

author position
  • last author4

Across the 4 of 4 papers where every author was matched, so the position is known.

fields
  • stat.ML2
  • cs.CR1
  • cs.LG1

identity via Semantic Scholar / OpenAlex

most citedThe effect of measurement error on clustering algorithms

3 citations · 3 across the 2 of their papers we have counts for

collaborators

4 papers

cs.CR2021

Privacy preserving local analysis of digital trace data: A proof-of-concept

Laura Boeschoten, Adriënne Mendrik, Emiel van der Veen +4

We present PORT, a software platform for local data extraction and analysis of digital trace data. While digital trace data collected by private and public parties hold a huge pote…

cs.LG2020

Multimodal Learning for Cardiovascular Risk Prediction using EHR Data

Ayoub Bagheri, T. Katrien J. Groenhof, Wouter B. Veldhuis +3

Electronic health records (EHRs) contain structured and unstructured data of significant clinical and research value. Various machine learning approaches have been developed to emp…

stat.ML2020★ 3 cited

The effect of measurement error on clustering algorithms

Paulina Pankowska, Daniel L. Oberski

Clustering consists of a popular set of techniques used to separate data into interesting groups for further analysis. Many data sources on which clustering is performed are well-k…

stat.ML2020

Fair inference on error-prone outcomes

Laura Boeschoten, Erik-Jan van Kesteren, Ayoub Bagheri +1

Fair inference in supervised learning is an important and active area of research, yielding a range of useful methods to assess and account for fairness criteria when predicting gr…

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