NIPS Proceedingsβ

Joshua B. Tenenbaum

31 Papers

  • Learning to Learn with Compound HD Models (2011)
  • Dynamic Infinite Relational Model for Time-varying Relational Data Analysis (2010)
  • Nonparametric Bayesian Policy Priors for Reinforcement Learning (2010)
  • Explaining human multiple object tracking as resource-constrained approximate inference in a dynamic probabilistic model (2009)
  • Help or Hinder: Bayesian Models of Social Goal Inference (2009)
  • Modelling Relational Data using Bayesian Clustered Tensor Factorization (2009)
  • Perceptual Multistability as Markov Chain Monte Carlo Inference (2009)
  • A Bayesian Framework for Cross-Situational Word-Learning (2007)
  • Learning and using relational theories (2007)
  • Causal inference in sensorimotor integration (2006)
  • Combining causal and similarity-based reasoning (2006)
  • Learning annotated hierarchies from relational data (2006)
  • Multiple timescales and uncertainty in motor adaptation (2006)
  • Bayesian models of human action understanding (2005)
  • Integrating Topics and Syntax (2004)
  • Parametric Embedding for Class Visualization (2004)
  • From Algorithmic to Subjective Randomness (2003)
  • Hierarchical Topic Models and the Nested Chinese Restaurant Process (2003)
  • Semi-Supervised Learning with Trees (2003)
  • Bayesian Models of Inductive Generalization (2002)
  • Dynamical Causal Learning (2002)
  • Global Versus Local Methods in Nonlinear Dimensionality Reduction (2002)
  • Theory-Based Causal Inference (2002)
  • Using Vocabulary Knowledge in Bayesian Multinomial Estimation (2001)
  • Structure Learning in Human Causal Induction (2000)
  • Rules and Similarity in Concept Learning (1999)
  • Bayesian Modeling of Human Concept Learning (1998)
  • Mapping a Manifold of Perceptual Observations (1997)
  • Separating Style and Content (1996)
  • Learning the Structure of Similarity (1995)
  • Factorial Learning by Clustering Features (1994)