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Computer Science

D-Index
59
Citations
28086
World Ranking
3326
National Ranking
1613

Overview

Mark Steyvers is a researcher primarily affiliated with the University of California, Irvine in the United States. Their work focuses on computer science, with a particular emphasis on artificial intelligence and related subfields. Steyvers has contributed significantly to topics such as explainable artificial intelligence (XAI), topic modeling, anomaly detection techniques, ethics and social impacts of AI, and machine learning methodologies.

The researcher's recent publications include:

  • Bayesian modeling of human-AI complementarity, 2022, Proceedings of the National Academy of Sciences
  • Three Challenges for AI-Assisted Decision-Making, 2023, Perspectives on Psychological Science
  • A Critical Review of Network-Based and Distributional Approaches to Semantic Memory Structure and Processes, 2021, Topics in Cognitive Science
  • What large language models know and what people think they know, 2025, Nature Machine Intelligence
  • AI-Assisted Decision-making: a Cognitive Modeling Approach to Infer Latent Reliance Strategies, 2022, Computational Brain & Behavior

Steyvers frequently publishes in venues such as:

  • arXiv (Cornell University)
  • SSRN Electronic Journal
  • Psychological Review
  • Proceedings of the National Academy of Sciences
  • Perspectives on Psychological Science

Their collaborative network includes multiple frequent co-authors, notably:

  • Padhraic Smyth
  • Aakriti Kumar
  • Heliodoro Tejeda
  • Disi Ji
  • Guy E. Hawkins

Steyvers' research spans these subfields within computer science:

  • Artificial Intelligence
  • Cognitive Neuroscience
  • Safety Research
  • Sociology and Political Science
  • Management Science and Operations Research

Their work covers main topics such as:

  • Explainable Artificial Intelligence (XAI)
  • Topic Modeling
  • Anomaly Detection Techniques and Applications
  • Ethics and Social Impacts of AI
  • Machine Learning and Data Classification
  • Intelligent Tutoring Systems and Adaptive Learning
  • Machine Learning and Algorithms

Best Publications

  • Finding scientific topics

    Thomas L. Griffiths;Mark Steyvers

  • Probabilistic Topic Models

    Mark Steyvers;Tom Griffiths

  • The large-scale structure of semantic networks: statistical analyses and a model of semantic growth.

    Mark Steyvers;Joshua B. Tenenbaum

  • The author-topic model for authors and documents

    Michal Rosen-Zvi;Thomas Griffiths;Mark Steyvers;Padhraic Smyth

  • Topics in semantic representation.

    Thomas L. Griffiths;Mark Steyvers;Joshua B. Tenenbaum

  • A model for recognition memory: REM—retrieving effectively from memory

    Richard M. Shiffrin;Mark Steyvers

  • Probabilistic author-topic models for information discovery

    Mark Steyvers;Padhraic Smyth;Michal Rosen-Zvi;Thomas Griffiths

  • Integrating Topics and Syntax

    Thomas L. Griffiths;Mark Steyvers;David M. Blei;Joshua B. Tenenbaum

  • Inferring causal networks from observations and interventions

    Mark Steyvers;Joshua B. Tenenbaum;Eric-Jan Wagenmakers;Ben Blum

  • Statistical topic models for multi-label document classification

    Timothy N. Rubin;America Chambers;Padhraic Smyth;Mark Steyvers

  • Learning author-topic models from text corpora

    Michal Rosen-Zvi;Chaitanya Chemudugunta;Thomas Griffiths;Padhraic Smyth

  • A probabilistic approach to semantic representation

    Thomas L Griffiths;Mark Steyvers

  • A method for efficiently sampling from distributions with correlated dimensions

    Brandon M. Turner;Per B. Sederberg;Scott D. Brown;Mark Steyvers

  • The sensitization and differentiation of dimensions during category learning.

    Robert L. Goldstone;Mark Steyvers

  • Modeling General and Specific Aspects of Documents with a Probabilistic Topic Model

    Chaitanya Chemudugunta;Padhraic Smyth;Mark Steyvers

  • A Bayesian analysis of human decision-making on bandit problems

    Mark Steyvers;Michael D. Lee;Eric-Jan Wagenmakers

  • Word Association Spaces for Predicting Semantic Similarity Effects in Episodic Memory.

    Mark Steyvers;Richard M. Shiffrin;Douglas L. Nelson

  • A Bayesian framework for simultaneously modeling neural and behavioral data

    Brandon M. Turner;Birte U. Forstmann;Eric-Jan Wagenmakers;Scott D. Brown

  • Google and the Mind Predicting Fluency With PageRank

    Thomas L. Griffiths;Mark Steyvers;Alana Firl

  • A Bayesian account of reconstructive memory.

    Pernille Hemmer;Mark Steyvers

  • The Large-Scale Structure of Semantic Networks

    M. Steyvers;J. Tenenbaum

Frequent Co-Authors

Padhraic Smyth
Padhraic Smyth University of California, Irvine
Thomas L. Griffiths
Thomas L. Griffiths Princeton University
Scott D. Brown
Scott D. Brown University of Newcastle Australia
Eric-Jan Wagenmakers
Eric-Jan Wagenmakers University of Amsterdam
David C. Atkins
David C. Atkins University of Washington
Zhong-Lin Lu
Zhong-Lin Lu New York University Shanghai
Richard M. Shiffrin
Richard M. Shiffrin Indiana University
Robert L. Goldstone
Robert L. Goldstone Indiana University
Jeroen G. W. Raaijmakers
Jeroen G. W. Raaijmakers University of Amsterdam

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