All Topics
Foundations
Causal Induction
Probabilistic Reasoning
Similarity and Categorization
Statistical Models of Language
Nonparametric Bayesian Models
Cultural Evolution and Iterated Learning
(Click on an author's name to view all papers by that author.)
Nonparametric Bayesian Models | |
| Blei, D. M., Griffiths, T. L., & Jordan, M. I. (in press). The nested Chinese restaurant process and Bayesian inference of topic hierarchies. Journal of the ACM. (manuscript pdf) |
| Kemp, C., Tenenbaum, J.B., Niyogi, S., & Griffiths, T. L. (in press). A probabilistic model of theory formation. Cognition. (doi) |
| 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) |
| 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) |
| 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) |
| Austerweil, J., & Griffiths, T. L. (2009). Analyzing human feature learning as nonparametric Bayesian inference. Advances in Neural Information Processing Systems 21. (pdf) |
| 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) |
| 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) |
| Navarro, D. J., & Griffiths, T. L. (2008). Latent features in similarity judgment: A nonparametric Bayesian approach. Neural Computation, 20, 2597-2628.(pdf) |
| 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. (manuscript pdf) |
| 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) |
| Wood, F., & Griffiths, T. L. (2007). Particle filtering for nonparametric Bayesian matrix factorization. Advances in Neural Information Processing Systems 19. (pdf) |
| 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) |
| 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) |
| Ghahramani, Z., Griffiths, T. L., & Sollich, P. (2007). Bayesian nonparametric latent feature models. Bayesian Statistics 8. Oxford University Press. (pdf) (discussion) (rejoinder) |
| 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) |
| 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) |
| 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) |
| 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) |
| 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) |
| Griffiths, T. L., & Ghahramani, Z. (2006). Infinite latent feature models and the Indian buffet process. Advances in Neural Information Processing Systems 18. (pdf) |
| 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) |
| 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) |
| 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) |
| 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) |