Noah D. Goodman

In fall 2010 I will join the faculty of Stanford University.
Visit my new website at Stanford.

I'm a Research Scientist in the Computational Cognitive Science Group at MIT.

My Curriculum Vitae.

Email: ndg at mit dot edu
Mail: MIT Building 46-4053, 77 Massachusetts Avenue, Cambridge, MA 02139


Software:

Information on the probabilistic programming language Church can be found on the Church wiki.

Manuscripts:

  • A formal model of number word acquisition. S. T. Piantadosi, N. D. Goodman, and J. B. Tenenbaum (in prep).
  • Learning a theory of causality. N. D. Goodman, T. D. Ullman, and J. B. Tenenbaum (under revision).
  • (Matlab code for the model.)

  • Learning grounded causal models. N. D. Goodman and J. B. Tenenbaum (under revision).
  • Rational reasoning in pedagogical contexts. P. Shafto, N. D. Goodman, and T. L. Griffiths (under revision).
  • Published:

  • Learning to learn causal models. C. Kemp, N. D. Goodman, and J. B. Tenenbaum (to appear). Cognitive Science.
  • Beyond Boolean logic: exploring representation languages for learning complex concepts. S. T. Piantadosi, J. B. Tenenbaum, and N. D. Goodman (2010). Proceedings of the Thirty-Second Annual Conference of the Cognitive Science Society.
  • Learning Structured Generative Concepts. A. Stuhlmueller, J. B. Tenenbaum, and N. D. Goodman (2010). Proceedings of the Thirty-Second Annual Conference of the Cognitive Science Society.
  • Prior expectations in pedagogical situations. P. Shafto, N. D. Goodman, B. Gerstle, and F. Ladusaw (2010). Proceedings of the Thirty-Second Annual Conference of the Cognitive Science Society.
  • Theory acquisition as stochastic search. T. D. Ullman, N. D. Goodman, and J. B. Tenenbaum (2010). Proceedings of the Thirty-Second Annual Conference of the Cognitive Science Society.
  • The structure and dynamics of scientific theories: a hierarchical Bayesian perspective. L. Henderson, N. D. Goodman, J. B. Tenenbaum, and J. Woodward (2010). Philosophy of Science.
  • Help or hinder: Bayesian models of social goal inference. T. Ullman, C. L. Baker, O. Macindoe, O. Evans, N. D. Goodman, and J. B. Tenenbaum (to appear). Advances in Neural Information Processing Systems 22.
  • The infinite latent events model. D. Wingate, N. D. Goodman, D. M. Roy, and J. B. Tenenbaum (2009). Uncertainty in Artificial Intelligence 2009.
  • Fragment grammars: Exploring computation and reuse in language T O'Donnell, N. D. Goodman, J. B. Tenenbaum (2009). Technical Report MIT-CSAIL-TR-2009-013, Massachusetts Institute of Technology.
  • Learning a theory of causality. N. D. Goodman, T. Ullman, and J. B. Tenenbaum (2009). Proceedings of the Thirty-First Annual Conference of the Cognitive Science Society.
  • Cause and intent: Social reasoning in causal learning. N. D. Goodman, C. L. Baker, and J. B. Tenenbaum (2009). Proceedings of the Thirty-First Annual Conference of the Cognitive Science Society.
  • How tall Is tall? Compositionality, statistics, and gradable adjectives. L. Schmidt, N. D. Goodman, D. Barner, and J. B. Tenenbaum (2009). Proceedings of the Thirty-First Annual Conference of the Cognitive Science Society.
  • One and done: Globally optimal behavior from locally suboptimal decisions. E. Vul, N. D. Goodman, T. L. Griffiths, J. B. Tenenbaum (2009). Proceedings of the Thirty-First Annual Conference of the Cognitive Science Society.
  • Informative communication in word production and word learning. M. C. Frank, N. D. Goodman, P. Lai, and J. B. Tenenbaum (2009). Proceedings of the Thirty-First Annual Conference of the Cognitive Science Society.
  • Continuity of discourse provides information for word learning. M. C. Frank, N. D. Goodman, J. B. Tenenbaum, and A. Fernald (2009). Proceedings of the Thirty-First Annual Conference of the Cognitive Science Society.
  • Using speakers' referential intentions to model early cross-situational word learning. M. C. Frank, N. D. Goodman, and J. B. Tenenbaum (2009). Psychological Science.
  • Going beyond the evidence: Abstract laws and preschoolers' responses to anomalous data. L. E. Schulz, N. D. Goodman, J. B. Tenenbaum, and A. Jenkins (2008). Cognition.
  • Church: a language for generative models. N. D. Goodman, V. K. Mansighka, D. Roy, K. Bonawitz, J. B. Tenenbaum (2008). Uncertainty in Artificial Intelligence 2008.
  • Random-World Semantics and Syntactic Independence for Expressive Languages. D. McAllester, B. Milch, N. D. Goodman (2008). Technical Report MIT-CSAIL-TR-2008-025, Massachusetts Institute of Technology.
  • Teaching games: statistical sampling assumptions for learning in pedagogical situations. P. Shafto, and N. D. Goodman (2008). Proceedings of the Thirtieth Annual Conference of the Cognitive Science Society.
  • A Bayesian Model of the Acquisition of Compositional Semantics. S. T. Piantadosi, N. D. Goodman, B. A. Ellis, and J. B. Tenenbaum (2008). Proceedings of the Thirtieth Annual Conference of the Cognitive Science Society.
  • Theory acquisition and the language of thought. C. Kemp, N. D. Goodman, and J. B. Tenenbaum (2008). Proceedings of the Thirtieth Annual Conference of the Cognitive Science Society.
  • Structured correlation from the causal background. R. Mayrhofer, N. D. Goodman, M. Waldmann, and J. B. Tenenbaum (2008). Proceedings of the Thirtieth Annual Conference of the Cognitive Science Society.
  • Modeling semantic cognition as logical dimensionality reduction. Y. Katz, N. D. Goodman, K. Kersting, C. Kemp, and J. B. Tenenbaum (2008). Proceedings of the Thirtieth Annual Conference of the Cognitive Science Society.
  • Theory-based social goal induction. C. L. Baker, N. D. Goodman, and J. B. Tenenbaum (2008). Proceedings of the Thirtieth Annual Conference of the Cognitive Science Society.
  • A Bayesian framework for cross-situational word-learning. M. C. Frank, N. D. Goodman, and J. B. Tenenbaum (2008). Advances in Neural Information Processing Systems 20.
  • Learning and using relational theories. C. Kemp, N. D. Goodman, and J. B. Tenenbaum (2008). Advances in Neural Information Processing Systems 20.
  • A rational analysis of rule-based concept learning. N. D. Goodman, J. B. Tenenbaum, J. Feldman, and T. L. Griffiths (2008). Cognitive Science. 32:1, 108-154.
  • Frameworks in science: a Bayesian approach. L. Henderson, N. D. Goodman, J. B. Tenenbaum, and J. Woodward. (This version was presented at the conference "Confirmation, Induction and Science", London School of Economics, March 2007.)
  • Compositionality in rational analysis: Grammar-based induction for concept learning. N. D. Goodman, J. B. Tenenbaum, T. L. Griffiths, and J. Feldman (in press). In M. Oaksford and N. Chater (Eds.). The probabilistic mind: Prospects for Bayesian cognitive science.
  • A rational analysis of rule-based concept learning. N. D. Goodman, T. L. Griffiths, J. Feldman, and J. B. Tenenbaum (2007). Proceedings of the Twenty-Ninth Annual Conference of the Cognitive Science Society.
  • Learning grounded causal models. N. D. Goodman, V. K. Mansinghka, and J. B. Tenenbaum (2007). Proceedings of the Twenty-Ninth Annual Conference of the Cognitive Science Society. (The experiment demo is here.) [Winner, 2007 Cognitive Science Society computational modeling prize for Perception and Action.]
  • Learning causal schemata. C. Kemp, N. D. Goodman, and J. B. Tenenbaum (2007). Proceedings of the Twenty-Ninth Annual Conference of the Cognitive Science Society. [Winner, 2007 Cognitive Science Society computational modeling prize for Higher-level Cognition.]
  • Intuitive theories of mind: A rational approach to false belief. Goodman, N. D., Bonawitz, E. B., Baker, C. L., Mansinghka, V. K, Gopnik, A., Wellman, H., Schulz, L. and Tenenbaum, J. B. (2006). Proceedings of the Twenty-Eighth Annual Conference of the Cognitive Science Society.
  • Slides from a few of my talks:

  • Lambda, the ultimate gamble. Slides from my Boston Lisp meeting talk.
  • Human concept learning as inductive programming. Slides from my invited talk at the International Conference on Inductive Logic Programming, Prague, September 2008.
  • Ideal observers in social cognition. Slides from my colloquium talk at the University of Salzburg, April 2007.
  • A shorter version:
  • Ideal observers in social cognition: a parable of rational analysis. Slides from my talk at the Harvard Mind Brain Behavior Conference, Cambridge, 2007.
  • Causal Learning and Learning to be Causal. Slides from my talk at the Society for Philosophy and Psychology, St. Louis, 2006.