Prediction and Semantic Association. - Thomas L. Griffiths 001; Mark Steyvers (NIPS, 2002)

Inferring causal networks from observations and interventions. - Mark Steyvers; Joshua B. Tenenbaum; Eric-Jan Wagenmakers; Ben Blum (Cogn. Sci., 2003)

Probabilistic author-topic models for information discovery. - Mark Steyvers; Padhraic Smyth; Michal Rosen-Zvi; Thomas Griffiths 001 (KDD, 2004)

Integrating Topics and Syntax. - Thomas L. Griffiths 001; Mark Steyvers; David M. Blei; Joshua B. Tenenbaum (NIPS, 2004)

The Author-Topic Model for Authors and Documents. - Michal Rosen-Zvi; Thomas L. Griffiths 001; Mark Steyvers; Padhraic Smyth (UAI, 2004)

Prediction and Change Detection. - Mark Steyvers; Scott D. Brown (NIPS, 2005)

The Large-Scale Structure of Semantic Networks: Statistical Analyses and a Model of Semantic Growth. - Mark Steyvers; Joshua B. Tenenbaum (Cogn. Sci., 2005)

Analyzing Entities and Topics in News Articles Using Statistical Topic Models. - David Newman 001; Chaitanya Chemudugunta; Padhraic Smyth; Mark Steyvers (ISI, 2006)

Modeling General and Specific Aspects of Documents with a Probabilistic Topic Model. - Chaitanya Chemudugunta; Padhraic Smyth; Mark Steyvers (NIPS, 2006)

Combining concept hierarchies and statistical topic models. - Chaitanya Chemudugunta; Padhraic Smyth; Mark Steyvers (CIKM, 2008)

Modeling Documents by Combining Semantic Concepts with Unsupervised Statistical Learning. - Chaitanya Chemudugunta; America Holloway; Padhraic Smyth; Mark Steyvers (ISWC, 2008)

Text Modeling using Unsupervised Topic Models and Concept Hierarchies - Chaitanya Chemudugunta; Padhraic Smyth; Mark Steyvers (CoRR, 2008)

The Wisdom of Crowds in the Recollection of Order Information. - Mark Steyvers; Michael D. Lee 001; Brent Miller; Pernille Hemmer (NIPS, 2009)

Modeling Human Performance in Restless Bandits with Particle Filters. - Sheng Kung Michael Yi; Mark Steyvers; Michael D. Lee 001 (J. Probl. Solving, 2009)

A Bayesian Account of Reconstructive Memory. - Pernille Hemmer; Mark Steyvers (Top. Cogn. Sci., 2009)

Learning concept graphs from text with stick-breaking priors. - America Chambers; Padhraic Smyth; Mark Steyvers (NIPS, 2010)

Learning author-topic models from text corpora. - Michal Rosen-Zvi; Chaitanya Chemudugunta; Thomas L. Griffiths 001; Padhraic Smyth; Mark Steyvers (ACM Trans. Inf. Syst., 2010)

A Model-Based Approach to Measuring Expertise in Ranking Tasks. - Michael D. Lee 001; Mark Steyvers; Mindy de Young; Brent Miller (CogSci, 2011)

The Wisdom of Crowds with Communication. - Brent Miller; Mark Steyvers (CogSci, 2011)

A Model of Concept Generalization and Feature Representation in Hierarchies. - Timothy N. Rubin; Matthew D. Zeigenfuse; Mark Steyvers (CogSci, 2011)

Using Inverse Planning and Theory of Mind for Social Goal Inference. - Sean Tauber; Mark Steyvers (CogSci, 2011)

A Psychological Model for Aggregating Judgments of Magnitude. - Edgar C. Merkle; Mark Steyvers (SBP, 2011)

Psychological models of human and optimal performance in bandit problems. - Michael D. Lee 001; Shunan Zhang; Miles Munro; Mark Steyvers (Cogn. Syst. Res., 2011)

Combining Background Knowledge and Learned Topics. - Mark Steyvers; Padhraic Smyth; Chaitanya Chemudugunta (Top. Cogn. Sci., 2011)

The Aggregative Contingent Estimation System: Selecting, Rewarding, and Training Experts in a Wisdom of Crowds Approach to Forecasting. - Dirk B. Warnaar; Edgar C. Merkle; Mark Steyvers; Thomas S. Wallsten; Eric R. Stone; David V. Budescu; J. Frank Yates; Winston R. Sieck; Hal R. Arkes; Chris F. Argenta; Youngwon Shin; Jennifer N. Carter (AAAI Spring Symposium: Wisdom of the Crowd, 2012)

Route choice in individuals - semantic network navigation. - Nicole Beckage; Mark Steyvers; Carter T. Butts (CogSci, 2012)

Context Effects in Multi-Alternative Decision Making: Empirical Data and a Bayesian Model. - Guy Hawkins; Scott D. Brown; Mark Steyvers; Eric-Jan Wagenmakers (Cogn. Sci., 2012)

The Wisdom of the Crowd in Combinatorial Problems. - Sheng Kung Michael Yi; Mark Steyvers; Michael D. Lee 001; Matthew J. Dry (Cogn. Sci., 2012)

Detecting authorship deception: a supervised machine learning approach using author writeprints. - Lisa Pearl; Mark Steyvers (Lit. Linguistic Comput., 2012)

Statistical topic models for multi-label document classification. - Timothy N. Rubin; America Chambers; Padhraic Smyth; Mark Steyvers (Mach. Learn., 2012)

Inferring Expertise in Knowledge and Prediction Ranking Tasks. - Michael D. Lee 001; Mark Steyvers; Mindy de Young; Brent Miller (Top. Cogn. Sci., 2012)

Inferring Subjective Prior Knowledge: An Integrative Bayesian Approach. - Sean Tauber; Mark Steyvers (CogSci, 2013)

Scoring Workers in Crowdsourcing: How Many Control Questions are Enough? - Qiang Liu 001; Alexander Ihler; Mark Steyvers (NIPS, 2013)

Choosing a Strictly Proper Scoring Rule. - Edgar C. Merkle; Mark Steyvers (Decis. Anal., 2013)

A Bayesian framework for simultaneously modeling neural and behavioral data. - Brandon M. Turner; Birte U. Forstmann; Eric-Jan Wagenmakers; Scott D. Brown; Per B. Sederberg; Mark Steyvers (NeuroImage, 2013)

A Hierarchical Adaptive Approach to the Optimal Design of Experiments. - Woojae Kim; Mark A. Pitt; Zhong-Lin Lu; Mark Steyvers; Hairong Gu; Jay I. Myung (CogSci, 2014)

Forecast aggregation via recalibration. - Brandon M. Turner; Mark Steyvers; Edgar C. Merkle; David V. Budescu; Thomas S. Wallsten (Mach. Learn., 2014)

A Hierarchical Adaptive Approach to Optimal Experimental Design. - Woojae Kim; Mark A. Pitt; Zhong-Lin Lu; Mark Steyvers; Jay I. Myung (Neural Comput., 2014)

The Roles of Knowledge and Memory in Generating Top-10 Lists. - Michael D. Lee 001; Emily Liu; Mark Steyvers (CogSci, 2015)

Why more is better: Simultaneous modeling of EEG, fMRI, and behavioral data. - Brandon M. Turner; Christian A. Rodriguez; Anthony M. Norcia; Samuel M. McClure; Mark Steyvers (NeuroImage, 2016)

A Bayesian model of knowledge and metacognitive control: Applications to opt-in tasks. - Stephen Bennett; Aaron S. Benjamin; Mark Steyvers (CogSci, 2017)

Leveraging Response Consistency within Individuals to Improve Group Accuracy for Rank-Ordering Problems. - Brent Miller; Mark Steyvers (CogSci, 2017)

Content Coding of Psychotherapy Transcripts Using Labeled Topic Models. - Garren Gaut; Mark Steyvers; Zac E. Imel; David C. Atkins; Padhraic Smyth (IEEE J. Biomed. Health Informatics, 2017)

Modeling the Covariance Structure of Complex Datasets Using Cognitive Models: An Application to Individual Differences and the Heritability of Cognitive Ability. - Nathan J. Evans; Mark Steyvers; Scott D. Brown (Cogn. Sci., 2018)

An Insight into Language: Investigating Lexical and Morphological Effects in Compound Remote Associate Problem Solving. - Alexander H. Bower; Andrew Burton; Mark Steyvers; William H. Batchelder (CogSci, 2019)

Distant Concept Connectivity in Network-Based and Spatial Word Representations. - Abhilasha Ashok Kumar; David A. Balota; Mark Steyvers (CogSci, 2019)

A Picture is Worth 7.17 Words: Learning Categories from Examples and Definitions. - Arsenii Moskvichev; Roman Tikhonov; Mark Steyvers (CogSci, 2019)

It's not the treasure, it's the hunt: Children are more explorative on an explore/exploit task than adults. - Emily S. Sumner; Mark Steyvers; Barbara W. Sarnecka (CogSci, 2019)

SAGE: A Hybrid Geopolitical Event Forecasting System. - Fred Morstatter; Aram Galstyan; Gleb Satyukov; Daniel Benjamin; Andrés Abeliuk; Mehrnoosh Mirtaheri; K. S. M. Tozammel Hossain; Pedro A. Szekely; Emilio Ferrara; Akira Matsui; Mark Steyvers; Stephen Bennett; et al. (IJCAI, 2019)

Predicting Task and Subject Differences with Functional Connectivity and Blood-Oxygen-Level-Dependent Variability. - Garren Gaut; Brandon M. Turner; Zhong-Lin Lu; Xiangrui Li; William A. Cunningham; Mark Steyvers (Brain Connect., 2019)

An Aha! Walks into a Bar: Joke Completion as a Form of Insight Problem Solving. - Alexander H. Bower; Mark Steyvers (CogSci, 2020)

Can I Trust My Fairness Metric? Assessing Fairness with Unlabeled Data and Bayesian Inference. - Disi Ji; Padhraic Smyth; Mark Steyvers (NeurIPS, 2020)

Active Bayesian Assessment of Black-Box Classifiers. - Disi Ji; Robert L. Logan IV; Padhraic Smyth; Mark Steyvers (AAAI, 2021)

The Funny Thing About Algorithm Aversion: Investigating Bias Toward AI Humor. - Alexander H. Bower; Mark Steyvers (CogSci, 2021)

Explaining Algorithm Aversion with Metacognitive Bandits. - Aakriti Kumar; Trisha Patel; Aaron S. Benjamin; Mark Steyvers (CogSci, 2021)

Combining Human Predictions with Model Probabilities via Confusion Matrices and Calibration. - Gavin Kerrigan; Padhraic Smyth; Mark Steyvers (NeurIPS, 2021)

Semantic Memory Search and Retrieval in a Novel Cooperative Word Game: A Comparison of Associative and Distributional Semantic Models. - Abhilasha Ashok Kumar; Mark Steyvers; David A. Balota (Cogn. Sci., 2021)

An Empirical Investigation of Reliance on AI-Assistance in a Noisy-Image Classification Task. - Heliodoro Tejeda Lemus; Aakriti Kumar; Mark Steyvers (HHAI, 2022)

A Critical Review of Network-Based and Distributional Approaches to Semantic Memory Structure and Processes. - Abhilasha Ashok Kumar; Mark Steyvers; David A. Balota (Top. Cogn. Sci., 2022)

Help me help you: A computational model for goal inference and action planning. - Abhilasha Ashok Kumar; Mark Steyvers (CogSci, 2023)

Capturing Humans' Mental Models of AI: An Item Response Theory Approach. - Markelle Kelly; Aakriti Kumar; Padhraic Smyth; Mark Steyvers (FAccT, 2023)

How Displaying AI Confidence Affects Reliance and Hybrid Human-AI Performance. - Heliodoro Tejeda Lemus; Aakriti Kumar; Mark Steyvers (HHAI, 2023)

When Do Drivers Intervene In Autonomous Driving? - Aakriti Kumar; Kumar Akash; Shashank Mehrotra; Teruhisa Misu; Mark Steyvers (HRI (Companion), 2023)

Human-AI collaboration (Conference Presentation). - Mark Steyvers (Image Perception, Observer Performance, and Technology Assessment, 2023)

Large Language Models Are Latent Variable Models: Explaining and Finding Good Demonstrations for In-Context Learning. - Xinyi Wang 003; Wanrong Zhu; Michael Saxon; Mark Steyvers; William Yang Wang (NeurIPS, 2023)

Hybrid forecasting of geopolitical events†. - Daniel M. Benjamin; Fred Morstatter; Ali E. Abbas; Andrés Abeliuk; Pavel Atanasov; Stephen Bennett; Andreas Beger; Saurabh Birari; David V. Budescu; Michele Catasta; Emilio Ferrara; Lucas Haravitch; et al. (AI Mag., 2023)

Bayesian Online Learning for Consensus Prediction. - Samuel Showalter; Alex J. Boyd; Padhraic Smyth; Mark Steyvers (AISTATS, 2024)

Learning with AI Assistance: A Path to Better Task Performance or Dependence? - Sheer Karny; Lukas William Mayer; Jackie Ayoub; Miao Song 007; Haotian Su; Danyang Tian; Ehsan Moradi-Pari; Mark Steyvers (CI, 2024)

Perceptions of Linguistic Uncertainty by Language Models and Humans. - Catarina G. Belém; Markelle Kelly; Mark Steyvers; Sameer Singh 001; Padhraic Smyth (EMNLP, 2024)

How do you know that you don't know? - Quentin F. Gronau; Mark Steyvers; Scott D. Brown (Cogn. Syst. Res., 2024)

The Calibration Gap between Model and Human Confidence in Large Language Models. - Mark Steyvers; Heliodoro Tejeda Lemus; Aakriti Kumar; Catarina G. Belém; Sheer Karny; Xinyue Hu; Lukas William Mayer; Padhraic Smyth (CoRR, 2024)

Hybrid Forecasting of Geopolitical Events. - Daniel M. Benjamin; Fred Morstatter; Ali E. Abbas; Andrés Abeliuk; Pavel Atanasov; Stephen Bennett; Andreas Beger; Saurabh Birari; David V. Budescu; Michele Catasta; Emilio Ferrara; Lucas Haravitch; et al. (CoRR, 2024)

What large language models know and what people think they know. - Mark Steyvers; Heliodoro Tejeda Lemus; Aakriti Kumar; Catarina G. Belém; Sheer Karny; Xinyue Hu; Lukas William Mayer; Padhraic Smyth (Nat. Mac. Intell., 2025)

Human-AI Collaboration: Trade-offs Between Performance and Preferences. - Lukas William Mayer; Sheer Karny; Jackie Ayoub; Miao Song 007; Danyang Tian; Ehsan Moradi-Pari; Mark Steyvers (CoRR, 2025)

Metacognition and Uncertainty Communication in Humans and Large Language Models. - Mark Steyvers; Megan A. K. Peters (CoRR, 2025)

Bayesian Inference for Correlated Human Experts and Classifiers. - Markelle Kelly; Alex Boyd; Samuel Showalter; Mark Steyvers; Padhraic Smyth (CoRR, 2025)

Beyond Accuracy: How AI Metacognitive Sensitivity improves AI-assisted Decision Making. - ZhaoBin Li; Mark Steyvers (CoRR, 2025)

When not to help: planning for lasting human-AI collaboration. - Mark Steyvers; Lukas William Mayer (CoRR, 2025)