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.)
Statistical Models of Language | |
| Rosen-Zvi, M., Chemudugunta, C., Griffiths, T., Smyth, P., & Steyvers, M. (in press). Learning author-topic models from text corpora. ACM Transactions on Information Systems. (pdf) |
| 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) |
| Reali, F., & Griffiths, T. L. (in press). Words as alleles: Connecting language evolution with Bayesian learners to models of genetic drift. Proceedings of the Royal Society, Series B. |
| Feldman, N. H., Griffiths, T. L., & Morgan, J. L. (2009). The influence of categories on perception: Explaining the perceptual magnet effect as optimal statistical inference. Psychological Review, 116, 752-782. (pdf) |
| Goldwater, S., Griffiths, T. L., & Johnson, M. (2009). A Bayesian framework for word segmentation: Exploring the effects of context. Cognition, 112, 21-54. (pdf) |
| Reali, F., & Griffiths, T. L. (2009). The evolution of linguistic frequency distributions: Relating regularization to inductive biases through iterated learning. Cognition, 111, 317-328. (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) |
| Bouchard-Cote, A., Griffiths, T. L., & Klein, D. (2009). Improved reconstruction of protolanguage word forms. Proceedings of the North American Conference on Computational Linguistics (NAACL'09). (pdf) |
| Canini, K. R., Shi, L., & Griffiths, T. L. (2009). Online inference of topics with Latent Dirichlet Allocation. AISTATS. (pdf) |
| Levy, R., Reali, F., & Griffths, T. L. (2009). Modeling the effects of memory on human online sentence processing with particle filters. Advances in Neural Information Processing Systems 21. (pdf) |
| Dowman, M., Savova, V., Griffiths, T. L., Kording, K. P., Tenenbaum, J. B., Purver, M. (2008). A probabilistic model of meetings that combines words and discourse features. IEEE Transactions on Audio, Speech, and Language Processing, 16, 1238-1248. (pdf) |
| Reali, F., & Griffiths, T. L. (2008). The evolution of frequency distributions: Relating regularization to inductive biases through iterated learning. Proceedings of the 30th Annual Conference of the Cognitive Science Society. (pdf) |
| Bouchard-Cote, A., Liang, P., Griffiths, T. L., & Klein, D. (2008). A probabilistic approach to language change. Advances in Neural Information Processing Systems 20. (pdf) |
| Steyvers, M., & Griffiths, T. L. (2008). Rational analysis as a link between human memory and information retrieval. 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., Steyvers, M., & Firl, A. (2007). Google and the mind: Predicting fluency with PageRank. Psychological Science, 18, 1069-1076. (pdf) |
| Iwata, T., Saito, K., Ueda, N., Stromsten, S., Griffiths, T. L., & Tenenbaum, J. B. (2007). Parametric embedding for class visualization. Neural Computation, 19, 2536-2556. (pdf) |
| Bouchard, A., Liang, P., Griffiths, T., & Klein, D. (2007). A probabilistic approach to diachronic phonology. Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL). (pdf) |
| Frank, M. C., Goldwater, S., Mansinghka, V., Griffiths, T., & Tenenbaum, J. (2007). Modeling human performance in statistical word segmentation. Proceedings of the Twenty-Ninth Annual Conference of the Cognitive Science Society. (pdf) |
| Feldman, N. H., & Griffiths, T. L. (2007). A rational account of the perceptual magnet effect. Proceedings of the Twenty-Ninth Annual Conference of the Cognitive Science Society. (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) |
| Goldwater, S., & Griffiths, T. L. (2007). A fully Bayesian approach to unsupervised part-of-speech tagging. Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics (ACL'07). (pdf) |
| Griffiths, T. L., Steyvers, M., & Tenenbaum, J. B. (2007). Topics in semantic representation. Psychological Review, 114,211-244. (pdf) (topic modeling toolbox) |
| Johnson, M., Griffiths, T. L., & Goldwater, S. (2007). Bayesian inference for PCFGs via Markov chain Monte Carlo. Proceedings of the North American Conference on Computational Linguistics (NAACL'07). (pdf) |
| Steyvers, M., & Griffiths, T. (2007). Probabilistic topic models. In T. Landauer, D. S. McNamara, S. Dennis, & W. Kintsch (Eds.), Handbook of Latent Semantic Analysis. Hillsdale, NJ: Erlbaum. (pdf) (topic modeling toolbox) |
| Goldwater, S., Griffiths, T. L., & Johnson, M. (2007). Distributional cues to word segmentation: Context is important. Proceedings of the 31st Boston University Conference on Language Development. (pdf) |
| Steyvers, M., Griffiths, T. L., & Dennis, S. (2006). Probabilistic inference in human semantic memory. Trends in Cognitive Science, 10, 327-334. (pdf) (topic modeling toolbox) |
| Purver, M., Kording, K. P., Griffiths, T. L., & Tenenbaum, J. B. (2006). Unsupervised topic modelling for multi-party spoken discourse. Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics. (pdf) |
| 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) |
| Goldwater, S., Griffiths, T. L., & Johnson, M. (2006). Interpolating between types and tokens by estimating power law generators. Advances in Neural Information Processing Systems 18. (pdf) (note: this version of the paper is slightly modified from the hardcopy proceedings) |
| Iwata, T., Saito, K., Ueda, N., Stromsten, S., Griffiths, T. L., & Tenenbaum, J. B. (2005). Parametric embedding for class visualization. Advances in Neural Information Processing Systems 17. (pdf) |
| Griffiths, T. L., Steyvers, M., Blei, D. M., & Tenenbaum, J. B. (2005). Integrating topics and syntax. Advances in Neural Information Processing Systems 17. (pdf) (topic modeling toolbox) |
| Steyvers, M., Smyth, P., Rosen-Zvi, M., & Griffiths, T. (2004). Probabilistic Author-Topic models for information discovery. The Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. (pdf) (demo) (topic modeling toolbox) |
| Rosen-Zvi, M., Griffiths, T., Steyvers, M., & Smyth, P. (2004). The Author-Topic Model for authors and documents. 20th Conference on Uncertainty in Artificial Intelligence. (pdf) (demo) (topic modeling toolbox) |
| Griffiths, T. L., & Steyvers, M. (2004). Finding scientific topics. Proceedings of the National Academy of Sciences, 101, 5228-5235. (pdf) (topic modeling toolbox) |
| 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) |
| Griffiths, T. L., & Steyvers, M. (2003). Prediction and semantic association. Advances in Neural Information Processing Systems 15. (pdf) (topic modeling toolbox) |
| Griffiths, T. L., & Tenenbaum, J. B. (2002). Using vocabulary knowledge in Bayesian multinomial estimation. Advances in Neural Information Processing Systems 14. (pdf) |
| Griffiths, T. L., & Steyvers, M. (2002). A probabilistic approach to semantic representation. Proceedings of the 24th Annual Conference of the Cognitive Science Society. (pdf) (topic modeling toolbox) |