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F Ying, L. , Collins, K. M. , Wong, L. , Sucholutsky, I. , Liu, R. , Weller, A. , Shu, T. , Griffiths, T. L. , & Tenenbaum, J. B. (2025). On benchmarking human-like intelligence in machines. (preprint)
CEIL F Allen, K. , Brändle, F. , Botvinick, M. , Fan, J. E. , Gershman, S. J. , Gopnik, A. , Griffiths, T. L. , Hartshorne, J. K. , Hauser, T. U. , Ho, M. , de Leeuw, J. R. , Ma, W. J. , Murayama, K. , Nelson, J. D. , van Opheusden, B. , Pouncy, T. , Rafner, J. , Rahwan, I. , Rutledge, R. B. , Sherson, J. , Şimşek, Ö. , Spiers, H. , Summerfield, C. , Thalmann, M. , Vélez, N. , Watrous, A. J. , Tenenbaum, J. B. , & Schulz, E. (2024). Using games to understand the mind. Nature Human Behaviour, 8 , 1035–1043. (pdf)
F SC Collins, K. M. , Sucholutsky, I. , Bhatt, U. , Chandra, K. , Wong, L. , Lee, M. , Zhang, C. E. , Zhi-Xuan, T. , Ho, M. , Mansinghka, V. , Weller, A. , Tenenbaum, J. B. , & Griffiths, T. L. (2024). Building machines that learn and think with people. Nature Human Behaviour, 8 (10), 1851-1863. (pdf)
S&C SC Sucholutsky, I. , Collins, K. M. , Malaviya, M. , Jacoby, N. , Liu, W. , Sumers, T. R. , Korakakis, M. , Bhatt, U. , Ho, M. , Tenenbaum, J. B. , Love, B. , Pardos, Z. A. , Weller, A. , & Griffiths, T. L. (2024). Representational alignment supports effective machine teaching. (preprint)
F Sucholutsky, I. , Muttenthaler, L. , Weller, A. , Peng, A. , Bobu, A. , Kim, B. , Love, B. C. , Grant, E. , Achterberg, J. , Tenenbaum, J. B. , Collins, K. M. , Hermann, K. L. , Oktar, K. , Greff, K. , Hebart, M. N. , Jacoby, N. , Marjieh, R. , Geirhos, R. , Chen, S. , Kornblith, S. , Rane, S. , Konkle, T. , O'Connell, T. P. , Unterthiner, T. , Lampinen, A. K. , Müller, K.-R. , Toneva, M. , & Griffiths, T. L. (2023). Getting aligned on representational alignment. (preprint)
CEIL PR Krafft, P. M. , Shmueli, E. , Griffiths, T. L. , & Tenenbaum, J. B. (2021). Bayesian collective learning emerges from heuristic social learning. Cognition, 212 , 104469. (pdf)
PR Griffiths, T. L. , Daniels, D. , Austerweil, J. L. , & Tenenbaum, J. B. (2018). Subjective randomness as statistical inference. Cognitive Psychology , 103, 85-109. (pdf)
PR SML Gates, M. A. , Veuthey, T. L. , Tessler, M. H. , Smith, K. A. , Gerstenberg, T. , Bayet, L. , & Tenenbaum, J. B. (2018). Tiptoeing around it: Inference from absence in potentially offensive speech. Proceedings of the 40th Annual Conference of the Cognitive Science Society. (pdf)
CI P PR Hamrick, J. B. , Battaglia, P. W. , Griffiths, T. L. , Tenenbaum, J. B. (2016). Inferring mass in complex scenes by mental simulation. Cognition, 157, 61-76. (pdf)
F Goodman, N. D. , Frank, M. C. , Griffiths, T. L. , Tenenbaum, J. B. , Battaglia, P. , & Hamrick, J. B. (2015). Relevant and robust. A response to Marcus and Davis. Psychological Science, 26 , 539-541. (pdf)
PR RPM Vul, E. , Goodman, N. D. , Tenenbaum, J. B. , & Griffiths, T. L. (2014). One and done? Optimal decisions from very few samples. Cognitive Science, 38 , 599-637. (pdf)
F Griffiths, T. L. , Tenenbaum, J. B. , & Kemp, C. (2012). Bayesian inference. In K. J. Holyoak & R. G. Morrison, (Eds.) Oxford Handbook of Thinking and Reasoning. Oxford: Oxford University Press. (book)
PR Griffiths, T. L. , & Tenenbaum, J. B. (2011). Predicting the future as Bayesian inference: People combine prior knowledge with observations when estimating duration and extent. Journal of Experimental Psychology: General, 140 , 725-743. (pdf)
CI CD Griffiths, T. L. , Sobel, D. , Tenenbaum, J. B. , & Gopnik, A. (2011). Bayes and blickets: Effects of knowledge on causal induction in children and adults. Cognitive Science, 35 , 1407-1455. (pdf)
CD F Perfors, A. , Tenenbaum, J. B. , Griffiths, T. L. , & Xu, F. (2011). A tutorial introduction to Bayesian models of cognitive development. Cognition, 120 , 302-321. (pdf)
NBM S&C 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. W.ills (Eds.) Formal approaches in categorization. Cambridge, UK: Cambridge University Press. (book)
CD F Tenenbaum, J. B. , Kemp, C. , Griffiths, T. L. , & Goodman, N. D. (2011) How to grow a mind: Statistics, structure, and abstraction. Science, 331, 1279-1285. (pdf)
NBM SML Frank, M. , Goldwater, S. , Griffiths, T. L. , & Tenenbaum, J. B. (2010). Modeling human performance in statistical word segmentation. Cognition, 117, 107-125.(pdf)
F Griffiths, T. L. , Chater, N. , Kemp, C. , Perfors, A. , & Tenenbaum, J. B. (2010). Probabilistic models of cognition: Exploring representations and inductive biases. Trends in Cognitive Sciences, 14, 357-364. (pdf)
CI NBM Kemp, C. , Tenenbaum, J. B. , Niyogi, S. , & Griffiths, T. L. (2010). A probabilistic model of theory formation. Cognition, 114, 165-196. (pdf)
CI CD Griffiths, T. L. , & Tenenbaum, J. B. (2009). Theory-based causal induction. Psychological Review, 116, 661-716. (pdf)
PR RPM Vul, E. , Goodman, N. D. , Griffiths, T. L. , & Tenenbaum, J. B. (2009). One and done? Optimal decisions from very few samples. Proceedings of the 31st Annual Conference of the Cognitive Science Society. (pdf)
SML 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)
S&C Goodman, N. D. , Tenenbaum, J. B. , Feldman, J. , & Griffiths, T. L. (2008). A rational analysis of rule-based concept learning. Cognitive Science, 32, 108-154. (pdf)
S&C Goodman, N. D. , Tenenbaum, J. B. , Griffiths, T. L. , & Feldman, J. (2008). Compositionality in rational analysis: Grammar-based induction for concept learning. In M. Oaksford and N. Chater (Eds.). The probabilistic mind: Prospects for rational models of cognition. Oxford: Oxford University Press. (pdf)
F Griffiths, T. L. , Kemp, C. , & Tenenbaum, J. B. (2008). Bayesian models of cognition. In Ron Sun (ed.), The Cambridge handbook of computational cognitive modeling . Cambridge University Press. (pdf)
S&C SML 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)
SML Griffiths, T. L. , Steyvers, M. , & Tenenbaum, J. B. (2007). Topics in semantic representation. Psychological Review, 114, 211-244. (pdf) (topic modeling toolbox)
CI Tenenbaum, J. B. , Griffiths, T. L. , & Niyogi, S. (2007). Intuitive theories as grammars for causal inference. In A. Gopnik, & L. Schulz (Eds.), Causal learning: Psychology, philosophy, and computation. Oxford: Oxford University Press. (pdf)
CI Griffiths, T. L. , & Tenenbaum, J. B. (2007). Two proposals for causal grammars. In A. Gopnik & L. Schulz (Eds.), Causal learning: Psychology, philosophy, and computation. Oxford: Oxford University Press. (pdf)
CI PR Griffiths, T. L. , & Tenenbaum, J. B. (2007). From mere coincidences to meaningful discoveries. Cognition, 103, 180-226. (pdf)
SML Frank, M. C. , Goldwater, S. , Mansinghka, V. , Griffiths, T. , & Tenenbaum, J. B. (2007). Modeling human performance in statistical word segmentation. Proceedings of the Twenty-Ninth Annual Conference of the Cognitive Science Society. (pdf)
S&C Goodman, N. D. , Griffiths, T. L. , Feldman, J. , & Tenenbaum, J. B. (2007). A rational analysis of rule-based concept learning. Proceedings of the Twenty-Ninth Annual Conference of the Cognitive Science Society. (pdf)
PR Griffiths, T. L. , & Tenenbaum, J. B. (2006). Optimal predictions in everyday cognition. Psychological Science, 17, 767-773. (pdf) (article in The Economist )
CI F Tenenbaum, J. B. , Griffiths, T. L. , & Kemp, C. (2006). Theory-based Bayesian models of inductive learning and reasoning. Trends in Cognitive Science, 10, 309-318. (pdf)
F PR Griffiths, T. L. , & Tenenbaum, J. B. (2006). Statistics and the Bayesian mind. Significance, 3 , 130-133. (pdf)
SML 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)
NBM 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)
CI NBM 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)
CI Griffiths, T. L. , & Tenenbaum, J. B. (2005). Structure and strength in causal induction. Cognitive Psychology, 51, 354-384. (pdf) (Matlab code for computing causal support)
SML 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)
SML 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)
S&C Kemp, C. S , Griffiths, T. L. , Stromsten, S. , & Tenenbaum, J. B. (2004). Semi-supervised learning with trees. Advances in Neural Information Processing Systems 16. (pdf)
NBM SML 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)
PR Griffiths, T. L. , & Tenenbaum, J. B. (2004). From algorithmic to subjective randomness. Advances in Neural Information Processing Systems 16. (pdf) (winner of the Best Student Paper prize)
CI Kemp, C. , Griffiths, T. L. , & Tenenbaum, J. B. (2004). Discovering latent classes in relational data. AI Memo 2004-019 (pdf)
CI CD Griffiths, T. L. , Baraff, E. R. , & Tenenbaum, J. B. (2004). Using physical theories to infer hidden causal structure. Proceedings of the 26th Annual Conference of the Cognitive Science Society. (pdf)
CI Danks, D. , Griffiths, T. L. , & Tenenbaum, J. B. (2003). Dynamical causal learning. Advances in Neural Information Processing Systems 15. (pdf)
CI Tenenbaum, J. B. , & Griffiths, T. L. (2003). Theory-based causal inference. Advances in Neural Information Processing Systems 15. (pdf)
PR Griffiths, T. L. , & Tenenbaum, J. B. (2003). Probability, algorithmic complexity, and subjective randomness. Proceedings of the 25th Annual Conference of the Cognitive Science Society. (pdf)
SML Griffiths, T. L. , & Tenenbaum, J. B. (2002). Using vocabulary knowledge in Bayesian multinomial estimation. Advances in Neural Information Processing Systems, 14 . (pdf)
CI Tenenbaum, J. B. , & Griffiths, T. L. (2001). Structure learning in human causal induction. Advances in Neural Information Processing Systems 13 . (pdf) (Matlab code for computing causal support)
S&C Tenenbaum, J. B. , & Griffiths, T. L. (2001). Generalization, similarity, and Bayesian inference. Behavioral and Brain Sciences, 24, 629-641. (pdf)
S&C Tenenbaum, J. B. , & Griffiths, T. L. (2001). Some specifics about generalization. Behavioral and Brain Sciences, 24, 772-778. (html)
PR Griffiths, T. L. , & Tenenbaum, J. B. (2001). Randomness and coincidences: Reconciling intuition and probability theory. Proceedings of the 23rd Annual Conference of the Cognitive Science Society. (pdf)
PR Tenenbaum, J. B. , & Griffiths, T. L. (2001). The rational basis of representativeness. Proceedings of the 23rd Annual Conference of the Cognitive Science Society. (pdf)
PR Griffiths, T. L. , & Tenenbaum, J. B. (2000). Teacakes, trains, toxins, and taxicabs: A Bayesian account of predicting the future. Proceedings of the 22nd Annual Conference of the Cognitive Science Society . (pdf)