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Reza Shokri

Reza Shokri has 2 papers in our library, in 2016–2022, most of them on Language models & pretraining; often with Fatemehsadat Mireshghallah, Kartik Goyal, Taylor Berg-Kirkpatrick.

2 papers in our library · 2016–2022

OpenAlex · ORCID · h-index 40 · 102 works · 14,135 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 276 citations in our library · ⭐ Paper Star rank #3840

The profile is the library's match of the name; two people with one name are not always told apart.

Elsewhere: Google Scholar · Semantic Scholar · dblp · ACL Anthology

 Affiliations

🎓National University of SingaporeSingapore · Singapore2017–2026 · 43 paperscurrent🏢GoogleMountain View · United States2025–2026 · 3 paperscurrent🎓École Polytechnique Fédérale de LausanneLausanne · Switzerland2009–2012 · 13 papers🎓The University of Texas at AustinAustin · United States2015–2016 · 7 papers🎓University of TehranTehran · Iran2006–2008 · 7 papers🎓Cornell TechNew York · United States2016–2017 · 6 papers🎓ETH ZurichZurich · Switzerland2014 · 5 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 4 more institutions on single bylines are not listed.

 Concepts, shelves and topics

Of its works in our library: the number of works on each and its rating among them.

📚 Shelves 4 Artificial Intelligence · 2 works Computer Science · 2 works Social Sciences · 1 work Sociology and Political Science · 1 work

🧭 Topics 1 Language models & pretraining · 1 work

 Works by year

2020s

20221 paper

2010s

20161 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Membership Inference Attacks against Machine Learning Models (2016 · 233 citations)
    The paper asks whether a trained classifier reveals which individual records were in its training set. The attacker can only query the model as a black box and see its prediction vectors. The authors treat this as a supervised learning task. They build several "shadow models" that imitate the target and whose…
  2. Quantifying Privacy Risks of Masked Language Models Using Membership Inference Attacks (2022 · 145 citations)
    Earlier studies suggested that masked language models (MLMs) leak little of their training data. The authors argue this conclusion came from weak attacks that only threshold the target model's loss. Such a loss partly reflects how intrinsically hard a sample is, so it is a poor membership signal. They propose a…

 Co-authors here

4 co-authors with a page, the most papers together first.

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

 Cited by

Papers of our library cite their works 276 times (of the 141,840 papers whose bibliographies we read); 27 citing papers with a page here →.

Updated

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