Publications

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Perception Perception
Education Education
Causal Induction Causal Induction
Cognitive Development Cognitive Development
Probabilistic Reasoning Probabilistic Reasoning
Rational Process Models Rational Process Models
Similarity and Categorization Similarity and Categorization
Statistical Models of Language Statistical Models of Language
Nonparametric Bayesian Models Nonparametric Bayesian Models
Cultural Evolution and Iterated Learning Cultural Evolution and Iterated Learning

(Click on an author's name to view all papers by that author.)

Nonparametric Bayesian Models
Causal Induction
Nonparametric Bayesian Models
Lucas, C. G., Griffiths, T. L., Williams, J. J., & Kalish, M. L. (2015). A rational model of function learning. Psychonomic Bulletin and Review. (pdf)
Causal Induction
Statistical Models of Language
Nonparametric Bayesian Models
Buchsbaum, D., Griffiths, T. L., Plunkett, D., Gopnik, A., & Baldwin, D. (2015). Inferring action structure and causal relationships in continuous sequences of human action. Cognitive Psychology, 76, 30-77. (pdf)
Statistical Models of Language
Nonparametric Bayesian Models
Feldman, N. H., Griffiths, T. L., Goldwater, S., & Morgan, J. (2013). A role for the developing lexicon in phonetic category acquisition. Psychological Review, 120, 751-778. (pdf)
Similarity and Categorization
Nonparametric Bayesian Models
Austerweil, J., & Griffiths, T. L. (2013). A nonparametric Bayesian framework for constructing flexible feature representations. Psychological Review, 120, 817-851. (pdf)
Perception
Nonparametric Bayesian Models
Austerweil, J. L., Friesen, A. L., & Griffiths, T. L. (2011). An ideal observer model for identifying the reference frame of objects. Advances in Neural Information Processing Systems, 24. (pdf)
Perception
Nonparametric Bayesian Models
Austerweil, J. L., & Griffiths, T. L. (2011). A rational model of the effects of distributional information on feature learning. Cognitive Psychology, 63, 173-209. (pdf)
Statistical Models of Language
Nonparametric Bayesian Models
Goldwater, S., Griffiths, T. L., Johnson, M. (2011). Producing power-law distributions and damping word frequencies with two-stage language models. Journal of Machine Learning Research, 12, 2335-2382. (pdf)
Nonparametric Bayesian Models
Griffiths, T. L., & Ghahramani, Z. (2011). The Indian Buffet Process: An introduction and review. Journal of Machine Learning Research, 12, 1185-1224. (pdf)
Similarity and Categorization
Nonparametric Bayesian Models
Canini, K. R., & Griffiths, T. L. (2011). A nonparametric Bayesian model of multi-level category learning. Proceedings of the 25th AAAI Conference on Artificial Intelligence.(pdf)
Similarity and Categorization
Nonparametric Bayesian Models
Griffiths, T. L., Sanborn, A. N., Canini, K. R., Navarro, D. J., & Tenenbaum, J. B. (2011). Nonparametric Bayesian models of category learning. In E. M. Pothos & A. J. Wills (Eds.) Formal approaches in categorization. Cambridge, UK: Cambridge University Press. (book)
Statistical Models of Language
Nonparametric Bayesian Models
Frank, M., Goldwater, S., Griffiths, T. L., & Tenenbaum , J. B. (2010). Modeling human performance in statistical word segmentation. Cognition, 117, 107-125.(pdf)
Rational Process Models
Similarity and Categorization
Nonparametric Bayesian Models
Sanborn, A. N., Griffiths, T. L., & Navarro, D. J. (2010). Rational approximations to rational models: Alternative algorithms for category learning. Psychological Review, 117 (4), 1144-1167.(pdf)
Perception
Similarity and Categorization
Nonparametric Bayesian Models
Austerweil, J. L., & Griffiths, T. L. (2010). Learning invariant features using the Transformed Indian Buffet Process. Advances in Neural Information Processing Systems 23. (pdf)
Statistical Models of Language
Nonparametric Bayesian Models
Blei, D. M., Griffiths, T. L., & Jordan, M. I. (2010). The nested Chinese restaurant process and Bayesian nonparametric inference of topic hierarchies. Journal of the ACM, 57, 1-30.(pdf)
Causal Induction
Nonparametric Bayesian Models
Kemp, C., Tenenbaum, J. B., Niyogi, S., & Griffiths, T. L. (2010). A probabilistic model of theory formation. Cognition, 114, 165-196. (pdf)
Nonparametric Bayesian Models
Miller, K. T., Griffiths, T. L., & Jordan, M. I. (2009). Nonparametric latent feature models for link prediction. Advances in Neural Information Processing Systems 22. (pdf)
Perception
Similarity and Categorization
Nonparametric Bayesian Models
Austerweil, J. L., & Griffiths, T. L. (2009). The effect of distributional information on feature learning. Proceedings of the 31st Annual Conference of the Cognitive Science Society. (pdf)
Causal Induction
Statistical Models of Language
Nonparametric Bayesian Models
Buchsbaum, D., Griffiths, T. L., Gopnik, A., & Baldwin, D. (2009). Learning from actions and their consequences: Inferring causal variables from continuous sequences of human action. Proceedings of the 31st Annual Conference of the Cognitive Science Society. (pdf)
Statistical Models of Language
Nonparametric Bayesian Models
Feldman, N. H., Griffiths, T. L., & Morgan, J. L. (2009). Learning phonetic categories by learning a lexicon. Proceedings of the 31st Annual Conference of the Cognitive Science Society. (pdf)
Perception
Similarity and Categorization
Nonparametric Bayesian Models
Austerweil, J., & Griffiths, T. L. (2009). Analyzing human feature learning as nonparametric Bayesian inference. Advances in Neural Information Processing Systems 21. (pdf)
Nonparametric Bayesian Models
Miller, K. T., Griffiths, T. L., & Jordan, M. I. (2008). The phylogenetic Indian buffet process: A non-exchangeable nonparametric prior for latent features.Proceedings of the Twenty-Fourth Conference on Uncertainty in Artificial Intelligence (UAI 2008). (pdf)
Nonparametric Bayesian Models
Cultural Evolution and Iterated Learning
Xu, J., Reali, F., & Griffiths, T. L. (2008). A formal analysis of cultural evolution by replacement. Proceedings of the 30th Annual Conference of the Cognitive Science Society. (pdf)
Similarity and Categorization
Nonparametric Bayesian Models
Navarro, D. J., & Griffiths, T. L. (2008). Latent features in similarity judgment: A nonparametric Bayesian approach. Neural Computation, 20, 2597-2628.(pdf)
Similarity and Categorization
Nonparametric Bayesian Models
Griffiths, T. L., Sanborn, A. N., Canini, K. R., & Navarro, D. J. (2008). Categorization as nonparametric Bayesian density estimation. In M. Oaksford and N. Chater (Eds.). The probabilistic mind: Prospects for rational models of cognition. Oxford: Oxford University Press. (pdf)
Similarity and Categorization
Nonparametric Bayesian Models
Griffiths, T. L., Canini, K. R., Sanborn, A. N., & Navarro, D. J. (2007) Unifying rational models of categorization via the hierarchical Dirichlet process. Proceedings of the Twenty-Ninth Annual Conference of the Cognitive Science Society. (pdf)
Nonparametric Bayesian Models
Wood, F., & Griffiths, T. L. (2007). Particle filtering for nonparametric Bayesian matrix factorization. Advances in Neural Information Processing Systems 19. (pdf)
Statistical Models of Language
Nonparametric Bayesian Models
Johnson, M., Griffiths, T. L., & Goldwater, S. (2007). Adaptor grammars: A framework for specifying compositional nonparametric Bayesian models. Advances in Neural Information Processing Systems 19. (pdf)
Similarity and Categorization
Nonparametric Bayesian Models
Navarro, D. J., & Griffiths, T. L. (2007). A nonparametric Bayesian method for inferring features from similarity judgments. Advances in Neural Information Processing Systems 19. (pdf)
Nonparametric Bayesian Models
Ghahramani, Z., Griffiths, T. L., & Sollich, P. (2007). Bayesian nonparametric latent feature models. Bayesian Statistics 8. Oxford University Press. (pdf) (discussion) (rejoinder)
Statistical Models of Language
Nonparametric Bayesian Models
Goldwater, S., Griffiths, T. L., & Johnson, M. (2006). Contextual dependencies in unsupervised word segmentation. Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics.(pdf)
Rational Process Models
Similarity and Categorization
Nonparametric Bayesian Models
Sanborn, A. N., Griffiths, T. L., & Navarro, D. J. (2006). A more rational model of categorization. Proceedings of the 28th Annual Conference of the Cognitive Science Society. (pdf)
Nonparametric Bayesian Models
Kemp, C., Tenenbaum, J. B., Griffiths, T. L., Yamada, T., & Ueda, N. (2006). Learning systems of concepts with an infinite relational model. Proceedings of the Twenty-First National Conference on Artificial Intelligence (AAAI '06). (pdf) (IRM code)
Causal Induction
Nonparametric Bayesian Models
Mansinghka, V. K., Kemp, C., Tenenbaum, J. B., & Griffiths, T. L. (2006). Structured priors for structure learning. Proceedings of the Twenty-Second Conference on Uncertainty in Artificial Intelligence (UAI 2006). (pdf)
Causal Induction
Nonparametric Bayesian Models
Wood, F., Griffiths, T. L., & Ghahramani, Z. (2006). A non-parametric Bayesian method for inferring hidden causes. Proceedings of the Twenty-Second Conference on Uncertainty in Artificial Intelligence (UAI 2006). (pdf)
Nonparametric Bayesian Models
Griffiths, T. L., & Ghahramani, Z. (2006). Infinite latent feature models and the Indian buffet process. Advances in Neural Information Processing Systems 18. (pdf)
Nonparametric Bayesian Models
Navarro, D. J., Griffiths, T. L., Steyvers, M., & Lee, M. D. (2006). Modeling individual differences using Dirichlet processes. Journal of Mathematical Psychology, 50, 101-122. (pdf)
Nonparametric Bayesian Models
Navarro, D. J., Griffiths, T. L., Steyvers, M., & Lee, M. D. (2005). Modeling individual differences with Dirichlet processes. Proceedings of the 27th Annual Conference of the Cognitive Science Society. (pdf)
Nonparametric Bayesian Models
Griffiths, T. L., & Ghahramani, Z. (2005). Infinite latent feature models and the Indian buffet process. Gatsby Computational Neuroscience Unit Technical Report GCNU TR 2005-001. (pdf)
Statistical Models of Language
Nonparametric Bayesian Models
Blei, D. M., Griffiths, T. L., Jordan, M. I., & Tenenbaum, J. B. (2004). Hierarchical topic models and the nested Chinese restaurant process. Advances in Neural Information Processing Systems 16. (pdf) (winner of the Best Student Paper prize)

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