Publications from CoCoSci

Here are some representative publications published by Cocosci members. For a more complete list, please go to the homepage of the individual authors.

Nonlinear dimensionality reduction

• The Isomap Algorithm and Topological Stability. Balasubramanian, M., Shwartz, E. L., Tenenbaum, J. B., de Silva, V., and Langford, J. C. (2002) Science Jan 4 2002: 7.

• Global versus local methods in nonlinear dimensionality reduction. V. de Silva, J. B. Tenenbaum (2002), Advances in Neural Information Processing Systems 15. M.SBecker, S., Thrun, S., and Obermayer, K. (eds). Cambridge, MIT Press, 2002, 705-712.

• Unsupervised learning of curved manifolds. V. de Silva, J.B. Tenenbaum (2002) In D.D. Denison, M. H. Hansen, C. C. Holmes, B. Mallick and B. Yu (eds.), Nonlinear Estimation and Classification , Springer-Verlag, New York, 453-466.

• A global geometric framework for nonlinear dimensionality reduction. J. B. Tenenbaum, V. De Silva, J. C. Langford, Science 290 (5500): 22 December 2000. Website

• Mapping a manifold of perceptual observations. J. B. Tenenbaum (1998), Advances in Neural Information Processing Systems 10. Jordan, M., Kearns, M., and Solla, S. (eds). Cambridge, MIT Press, 1998, 682-687. (postscript)

Separating style and content

• Learning style translation for the lines of a drawing. W.T. Freeman, J.B. Tenenbaum, E. Pasztor (2003), ACM Transactions on Graphics 22 (1), January 2003, 33-46. (uncorrected proofs - SMALL pdf)

• Separating style and content with bilinear models. J. B. Tenenbaum, W. T. Freeman (2000), Neural Computation 12 (6), 1247-1283.
  
  

Concept learning and generalization

• Learning domain structures. Kemp, C. S., Perfors, A., and Tenenbaum, J. B. (2004). Proceedings of the Twenty-Sixth Annual Conference of the Cognitive Science Society.
  
• Semi-supervised learning with trees. Kemp, C., Griffiths, T. L, Stromsten, S., and Tenenbaum, J. B. (in press). Advances in Neural Information Processing Systems 16.
• Theory-based induction. Kemp, C. S. and Tenenbaum, J. B. (2003). Proceedings of the Twenty-Fifth Annual Conference of the Cognitive Science Society.
  

• Bayesian models of inductive generalization. N. Sanjana, J. B. Tenenbaum (2002), Advances in Neural Information Processing Systems 15. Becker, S., Thrun, S., Obermayer, K. (eds). Cambridge, MIT Press, 2002, 51-58.
• Rules and similarity in concept learning. J. B. Tenenbaum (2000), Advances in Neural Information Processing Systems 12. Solla, S., Leen, T. and Muller, K. (eds). Cambridge, MIT Press, 2000, 59-65. (postscript)
• Bayesian modeling of human concept learning. J. B. Tenenbaum (1999), Advances in Neural Information Processing Systems 11. Kearns, M., Solla, S., and Cohn, D. (eds). Cambridge, MIT Press, 1999, 59-68. (postscript)
• A Bayesian Framework for Concept Learning. J. B. Tenenbaum, Ph.D. Thesis, MIT, 1999
  
  

Learning Words

• Hierarchical topic models and the nested Chinese restaurant process. Blei, D., Griffiths, T. L, Jordan, M. I., and Tenenbaum, J. B. (in
  press). Advances in Neural Information Processing Systems 16.
  

• The large-scale structure of semantic networks: statistical analyses and a model of semantic growth. M. Steyvers, J. B. Tenenbaum, Submitted to Cognitive Science.

• Word learning as Bayesian inference. J. B. Tenenbaum, F. Xu (2000), Proceedings of the 22nd Annual Conference of the Cognitive Science Society (postscript)
  
  

Learning and similarity

• Learning the structure of similarity. J. B. Tenenbaum (1995), Advances in Neural Information Processing Systems 8. Toretzky, D., Mozer, M., and Hasselmo, M. (eds). Cambridge, MIT Press, 1995, 3-9. (postscript)

• Generalization, similarity, and Bayesian inference. J. B. Tenenbaum, T. L. Griffiths (2001), Behavioral and Brain Sciences, 24 pp. 629-641.

• Some specifics about generalization. J. B. Tenenbaum, T. L. Griffiths (2001), Behavioral and Brain Sciences, 24, pages 772-778. (html)
  

Probabilistic reasoning

• From algorithmic to subjective randomness. Griffiths, T. L. and Tenenbaum, J. B. (in press). Advances in Neural Information Processing Systems 16.

• The role of causal models in reasoning under uncertainty. Krynski, T. R. and Tenenbaum, J. B. (2003). Proceedings of the Twenty-Fifth Annual Conference of the Cognitive Science Society.
  

• V1 neurons signal acquisition of an internal representation of stimulus location. Sharma, J., Dragoi, V., Tenenbaum, J. B., Miller, E. K., and Sur, M. (2003). Science, 300, 1758-1763.
  

• Probability, algorithmic complexity, and subjective randomness. Griffiths, T. L. and Tenenbaum, J. B. (2003). Proceedings of the Twenty-Fifth Annual Conference of the Cognitive Science Society.

• The rational basis of representativeness. J. B. Tenenbaum, T. L. Griffiths (2001), 23rd Annual Conference of the Cognitive Science Society. 1036-1041.
  

• Randomness and coincidences: Reconciling intuition and probability theory. T. L. Griffiths, J. B. Tenenbaum (2001), 23rd Annual Conference of the Cognitive Science Society. 370-375.

• Teacakes, trains, toxins, and taxicabs: A Bayesian account of predicting the future. T. L. Griffiths, J. B. Tenenbaum (2000), Proceedings of the 22nd Annual Conference of the Cognitive Science Society. 202-207. (postscript)
  

Causal learning and inference

• Using physical theories to infer hidden causal structure. Griffiths, T.L., Baraff, E.R., & Tenenbaum, J.B. (2004). Proceedings of the Twenty-Sixth Annual Conference of the Cognitive Science Society.
  
• Learning causal laws. Tenenbaum, J. B. and Niyogi, S. (2003). Proceedings of the Twenty-Fifth Annual Conference of the Cognitive Science Society.
  
• Dynamical causal learning. Danks, D., Griffiths, T.L., & Tenenbaum, J.B. (2003). Advances in Neural Information Processing Systems 15. Becker, S., Thrun, S., and Obermayer. (eds). Cambridge, MIT Press, 2003, 67-74.
  

• Inferring causal networks from observations and interventions. M. Steyvers, J. B. Tenenbaum, E. J. Wagenmakers, B. Blum (2003), Cognitive Science 27: 453-489.

• Theory-based causal inference. J. B. Tenenbaum, T. L. Griffiths (2002), Advances in Neural Information Processing Systems 15. Becker, S., Thrun, S., and Obermayer. (eds). Cambridge, MIT Press, 2003, 35-42.

• The development of causal learning based on indirect evidence: More than associations. D. Sobel, J. B. Tenenbaum, A. Gopnik (2002), Submitted to Cognitive Science.
  NOTE: THIS IS A PREPUBLICATION DRAFT. PLEASE DO NOT CITE OR QUOTE WITHOUT PERMISSION, OR REDISTRIBUTE WITHOUT THIS NOTICE.
  

• Structure learning in human causal induction. J. B. Tenenbaum, T. L. Griffiths (2001), Advances in Neural Information Processing Systems 13. Leen, T., Dietterich, T., and Tresp, V., Cambridge, MIT Press, 2001, 59-65. (postscript)

© 2004 CocoSci