Charles Kemp

Structured statistical models of inductive reasoning (2009)

Inductive learning is impossible without overhypotheses, or constraints on the hypotheses considered by the learner. Some of these overhypotheses must be innate, but we suggest that hierarchical...

Learning and using relational theories (2009)

Everyday inductive inferences are often guided by rich background knowledge. Formal models of induction should aim to incorporate this knowledge, and should explain how different kinds of knowledge...

Opinion TRENDS in Cognitive Sciences Vol.10 No.7 July 2006 Special Issue: Probabilistic models of cognition Theory-based Bayesian models of inductive learning and reasoning (2008)

Much of human knowledge is organized into sophisticated systems that are often called intuitive theories. We propose that intuitive theories are mentally represented in a logical language, and that...

Joshua B. Tenenbaum, Thomas L. Griffiths, Charles Kemp

Inductive inference allows humans to make powerful generalizations from sparse data when learning about word meanings, unobserved properties, causal relationships, and many other aspects of the...

THIS IS A DRAFT VERSION. FINAL VERSION TO BE PUBLISHED AT NIPS ’06 Learning annotated hierarchies from relational data (2008)

Daniel M. Roy, Charles Kemp, Vikash K. Mansinghka, Joshua B. Tenenbaum

The objects in many real-world domains can be organized into hierarchies, where each internal node of a hierarchy picks out a category of objects. Given a collection of features and relations defined...

Now (2008)

Charles Kemp, Aaron Bernstein, Joshua B. Tenenbaum

Kemp et al. [1] argue that two objects are similar to the extent that they appear to have been produced by the same generative process. Here we show the stimuli used in our experiment (Figures 1 and...

Learning and using relational theories (2008)

Blackwell Publishing Ltd BAYESIAN SPECIAL SECTION Learning overhypotheses with hierarchical Bayesian models (2008)

Much of human knowledge is organized into sophisticated systems that are often called intuitive theories. We propose that intuitive theories are mentally represented in a logical language, and that...

Bayesian models of cognition (2008)

Inductive learning is impossible without overhypotheses, or constraints on the hypotheses considered by the learner. Some of these overhypotheses must be innate, but we suggest that hierarchical...

Thomas L. Griffiths, Charles Kemp, Joshua B. Tenenbaum

For over 200 years, philosophers and mathematicians have been using probability theory to describe human cognition. While the theory of probabilities was first developed as a means of analyzing games...

Learning Overhypotheses (2008)

Learning overhypotheses 2 (2008)

Inductive learning is impossible without overhypotheses, or constraints on the hypotheses considered by the learner. Some of these overhypotheses must be innate, but we suggest that hierarchical...

Charles Kemp, Charles Kemp, Amy Perfors, Joshua B. Tenenbaum

Special issue on “Probabilistic models of cognition (2008)

Joshua B. Tenenbaum, Thomas L. Griffiths, Charles Kemp

Inductive inference allows humans to make powerful generalizations from sparse data when learning about word meanings, unobserved properties, causal relationships, and many other aspects of the...

Opinion TRENDS in Cognitive Sciences Vol.10 No.7 July 2006 Special Issue: Probabilistic models of cognition Theory-based Bayesian models of inductive learning and reasoning (2008)

Joshua B. Tenenbaum, Thomas L. Griffiths, Charles Kemp

Inductive inference allows humans to make powerful generalizations from sparse data when learning about word meanings, unobserved properties, causal relationships, and many other aspects of the...

Learning and using relational theories: supporting material (2008)

Learning causal schemata (2007)

We previously considered a Bayesian model based on our complexity measure, but Equations 2 and 3 can also be used to define a Bayesian model based on Goodman’s measure. The left columns show...

Learning causal schemata (2007)

Causal inferences about sparsely observed objects are often supported by causal schemata, or systems of abstract causal knowledge. We present a hierarchical Bayesian framework that learns simple...

Certified by: Accepted by: (2007)

Causal inferences about sparsely observed objects are often supported by causal schemata, or systems of abstract causal knowledge. We present a hierarchical Bayesian framework that learns simple...

Charles Kemp, Joshua Tenenbaum, Charles Kemp

The acquisition of inductive constraints by

Learning systems of concepts with an infinite relational model (2006)

Combining causal and similarity-based reasoning. NIPS (2006)

Relationships between concepts account for a large proportion of semantic knowledge. We present a nonparametric Bayesian model that discovers systems of related concepts. Given data involving several...

Charles Kemp, Patrick Shafto, Allison Berke, Joshua B. Tenenbaum

Everyday inductive reasoning draws on many kinds of knowledge, including knowledge about relationships between properties and knowledge about relationships between objects. Previous accounts of...

Learning systems of concepts with an infinite relational model (2006)

Learning systems of concepts with an infinite relational model (2006)

Relationships between concepts account for a large proportion of semantic knowledge. We present a nonparametric Bayesian model that discovers systems of related concepts. Given data involving several...

Combining causal and similarity-based reasoning. NIPS (2006)

Relationships between concepts account for a large proportion of semantic knowledge. We present a nonparametric Bayesian model that discovers systems of related concepts. Given data involving several...

Charles Kemp, Patrick Shafto, Allison Berke, Joshua B. Tenenbaum

Everyday inductive reasoning draws on many kinds of knowledge, including knowledge about relationships between properties and knowledge about relationships between objects. Previous accounts of...

Learning annotated hierarchies from relational data (2006)

Daniel M. Roy, Charles Kemp, Vikash K. Mansinghka, Joshua B. Tenenbaum

The objects in many real-world domains can be organized into hierarchies, where each internal node picks out a category of objects. Given a collection of features and relations defined over a set of...

Nonsense and sensibility: inferring unseen possibilities (2006)

Lauren A. Schmidt, Charles Kemp, Joshua B. Tenenbaum

How do we distinguish the sensible yet unlikely (blue bananas) from the nonsensical (hour-long bananas), given observations only of what is true in the world (e.g., yellow bananas)? Judgments like...

Learning cross-cutting systems of categories (2006)

Patrick Shafto, Charles Kemp, Vikash Mansinghka, Matthew Gordon, Joshua B. Tenenbaum

Most natural domains can be represented in multiple ways: animals may be thought of in terms of their taxonomic groupings or their ecological niches and foods may be thought of in terms of their...

Theory-based Bayesian models of inductive learning and reasoning (2006)

Joshua B. Tenenbaum, Charles Kemp, Patrick Shafto

Theory-based Bayesian models of inductive reasoning 2 Theory-based Bayesian models of inductive reasoning

Combining causal and similarity-based reasoning. NIPS (2006)

Charles Kemp, Patrick Shafto, Allison Berke, Joshua B. Tenenbaum

Everyday inductive reasoning draws on many kinds of knowledge, including knowledge about causal relationships between properties and knowledge about similarity relationships between objects. Previous...

A generative theory of similarity (2005)

Charles Kemp, Aaron Bernstein, Joshua B. Tenenbaum

We propose that similarity judgments are inferences about generative processes, and that two objects appear similar when they are likely to have been generated by the same process. We present a...

Context-sensitive induction (2005)

Patrick Shafto, Charles Kemp, Elizabeth Baraff, John D. Coley, Joshua B. Tenenbaum

Different kinds of knowledge are relevant in different inductive contexts. Previous models of category-based induction have focused on judgments about taxonomic properties, but other kinds of models...

Context-sensitive induction (2005)

Patrick Shafto, Charles Kemp, Elizabeth Baraff, John D. Coley, Joshua B. Tenenbaum

Different kinds of knowledge are relevant in different inductive contexts. Previous models of category-based induction have focused on judgments about taxonomic properties, but other kinds of models...

A generative theory of similarity (2005)

Charles Kemp, Aaron Bernstein, Joshua B. Tenenbaum

We propose that similarity judgments are inferences about generative processes, and that two objects appear similar when they are likely to have been generated by the same process. We present a...

Discovering Latent Classes in Relational Data (2004)

Kemp, Charles, Griffiths, Thomas L., Tenenbaum, Joshua B.

We present a framework for learning abstract relational knowledge with the aimof explaining how people acquire intuitive theories of physical, biological, orsocial systems. Our approach is based on a...

Discovering Latent Classes in Relational Data (2004)

Kemp, Charles, Griffiths, Thomas L., Tenenbaum, Joshua B.

We present a framework for learning abstract relational knowledge with the aimof explaining how people acquire intuitive theories of physical, biological, orsocial systems. Our approach is based on a...

Learning domain structures (2004)

Learning domain structures (2004)

How do people acquire and use knowledge about domain structures, such as the tree-structured taxonomy of folk biology? These structures are typically seen either as consequences of innate...

Semi-supervised learning with trees (2004)

How do people acquire and use knowledge about domain structures, such as the tree-structured taxonomy of folk biology? These structures are typically seen either as consequences of innate...

Charles Kemp, Thomas L. Griffiths, Sean Stromsten, Joshua B. Tenenbaum

We describe a nonparametric Bayesian approach to generalizing from few labeled examples, guided by a larger set of unlabeled objects and the assumption of a latent tree-structure to the domain. The...

Discovering latent classes in relational data (2004)

Charles Kemp, Thomas L. Griffiths, Joshua B. Tenenbaum

We present a framework for learning abstract relational knowledge with the aim of explaining how people acquire intuitive theories of physical, biological, or social systems. Our approach is based on...

International Journal of Humanoid Robotics c ○ World Scientific Publishing Company SENSING AND MANIPULATING BUILT-FOR-HUMAN ENVIRONMENTS (2004)

Rodney Brooks, Lijin Aryan, Aaron Edsinger, Paul Fitzpatrick, Charles Kemp, Eduardo Torres-jara, ...

We report on a dynamically balancing robot with a dexterous arm designed to operate in built-for-human environments. Our initial target task has been for the robot to navigate, identify doors, open...

Theory-based induction (2003)

The whole world in your hand: Active and interactive segmentation (2003)

We show how an abstract domain theory can be incorporated into a rational statistical model of induction. In particular, we describe a Bayesian model of category-based induction, and generate the...

Artur Arsenio, Paul Fitzpatrick, Charles Kemp, Giorgio Metta

This paper presents three approaches to object segmentation, a fundamental problem in computer vision. Each approach is aided by the presence of a hand or arm in the proximity of the object to be...

Theory-based induction (2003)

Semi-Supervised Learning with Trees (2003)

We show how an abstract domain theory may be incorporated into a rational statistical model of induction, giving a more principled basis for the model's assumptions, greater explanatory power,...

Charles Kemp, Thomas L. Griffiths, Sean Stromsten, Joshua B. Tenenbaum

We describe a nonparametric Bayesian approach to generalizing from few labeled examples, guided by a larger set of unlabeled objects and the assumption of a latent tree-structure to the domain. The...

Theory-Based Induction (2003)