8:00 - 8:45

Check-in & Continental Breakfast

8:45 - 9:00

Introductory Remarks

9:00 - 10:00

Josh Tenenbaum (Massachusetts Institute of Technology)

Introduction to Probabilistic Models of Cognition

10:00 - 10:15

Break

10:15 - 11:15

Josh Tenenbaum (Massachusetts Institute of Technology)

Basic Bayes: model fitting, model selection, and model averaging

11:15 - 11:30

Break

11:30 - 12:30

Nick Chater (University College London)

From deduction and symbols to induction and probabilities

12:30 - 1:30

Lunch (on your own)

1:45 - 2:45

Tom Griffiths (University of California, Berkeley (UC Berkeley))

Introduction to Graphical Models

3:00 - 4:00

Robert Jacobs (University of Rochester)

Bayesian Decision Theory

4:00 - 4:30

Break

4:30 - 5:30

Ying Nian Wu (UCLA)

Information Theory

5:30 - 7:00

Opening Reception hosted by IPAM (IPAM Building)

7:00

*Optional Evening Session - Nick Chater, Tom Griffiths, Josh Tenenbaum, "Subjective probability and Bayesian foundations"

Matlab tutorial, by Amy Perfors

8:00 - 9:00

Continental Breakfast

9:00 - 10:00

Alan Yuille (University of California, Los Angeles (UCLA))

Graphical Models in Vision

10:00 - 10:15

Break

10:15 - 11:15

Alan Yuille (University of California, Los Angeles (UCLA))

HMMs and inference in graphical models I

11:15 - 11:30

Break

11:30 - 12:30

Robert Jacobs (University of Rochester)

Ideal Observers and Ideal Actors

12:30 - 1:30

Lunch (on your own)

1:45 - 2:45

Hongjing Lu (University of Hong Kong)

Comparisons between human performance and model predictions in motion perception

3:00 - 4:00

Alan Yuille (University of California, Los Angeles (UCLA))

HMMs and inference in graphical models II

4:00 - 4:30

Break

4:30 - 5:30

Robert Jacobs (University of Rochester)

Sensory Cue Combination

7:00

*Optional Evening Session - Introduction to software for Bayesian modeling: SamIAm, Bayes Net Toolbox (BNT)

8:00 - 9:00

Continental Breakfast

9:00 - 10:00

Zoubin Ghahramani (University of Cambridge)

Probabilistic generative models and unsupervised learning I

10:00 - 10:15

Break

10:15 - 11:15

Zoubin Ghahramani (University of Cambridge)

Probabilistic generative models and unsupervised learning II

11:15 - 11:30

Break

11:30 - 12:30

Zoubin Ghahramani (University of Cambridge)

Nonparametric Bayesian Models

12:30 - 1:30

Lunch (on your own)

1:45 - 2:45

Breakout Session

Zoubin Ghahramani
Questions on generative models and nonparametric Bayes

3:00 - 4:00

Tom Griffiths (University of California, Berkeley (UC Berkeley))

Modern Monte Carlo methods I: Importance sampling and sequential Monte Carlo

4:00 - 4:30

Break

4:30 - 5:30

Tom Griffiths (University of California, Berkeley (UC Berkeley))

Modern Monte Carlo methods II: Markov chain Monte Carlo

7:00

*Optional Evening Session - Nick Chater, "Scale invariance in human cognition"
PowerPoint Presentation

8:00 - 9:00

Continental Breakfast

9:00 - 10:30

Zoubin Ghahramani (University of Cambridge)

Bayesian supervised learning and semi-supervised learning

10:30 - 11:00

Break

11:00 - 12:30

Ying Nian Wu (UCLA)

Hierarchical Bayesian Models

12:30 - 1:30

Lunch (on your own)

Charles Kemp, Amy Perfors, Josh Tenenbaum
Learning overhypotheses with hierarchical Bayesian models

1:45 - 2:45

Breakout Sessions

Zoubin Ghahramani, Yingnian Wu
Technical questions on Bayesian models

Tom Griffiths
MCMC with humans, and other questions on MCMC

"Practical experiments with simple Bayesian models", Amy Perfors

3:00 - 4:15

Josh Tenenbaum (Massachusetts Institute of Technology)

Hierarchical Bayesian models of human inductive learning

4:15 - 4:30

Break

4:30 - 5:30

Nick Chater (University College London)

Information theory, minimum description length, and human cognition

7:00

*Optional Evening Session - Michael Lee, "Bayesian model theoretics and BUGS"

8:00 - 9:00

Continental Breakfast

9:00 - 9:45

Ying Nian Wu (UCLA)

Causal Models

9:50 - 10:45

Tom Griffiths (University of California, Berkeley (UC Berkeley))

Graphical models and human causal learning

10:45 - 11:15

Break

11:15 - 12:00

Hongjing Lu (University of Hong Kong)

Model selection versus parameter estimation in Bayesian inference: An application to causal learning

12:05 - 12:50

Patricia Cheng (University of California, Los Angeles (UCLA))

Causal Reasoning in Humans and Rats

12:50 - 2:00

Lunch (on your own)

Breakout Sessions

Ed Vul
"Bayes Net Toolbox causal model demo"

2:00 - 3:00

Alison Gopnik (University of California, Berkeley (UC Berkeley))

Causal Learning in Children

3:15 - 4:00

Tom Griffiths (University of California, Berkeley (UC Berkeley))

Modeling causal learning in children

4:00 - 4:30

Break

4:30 - 5:30

Keith Holyoak (University of California, Los Angeles (UCLA))

From Causal Models to Analogical Inference

8:00 - 9:00

Check-in & Continental Breakfast

9:00 - 10:00

Stuart Russell (University of California, Berkeley (UC Berkeley))

Probability for Worlds with Things in Them

10:00 - 10:15

Break

10:15 - 11:15

Brian Milch (Massachusetts Institute of Technology)

Relational Probability Models

11:15 - 11:30

Break

11:30 - 12:30

Brian Milch (Massachusetts Institute of Technology)

Unknown Objects and BLOG

12:30 - 1:30

Lunch (on your own)

1:45 - 2:45

Breakout Sessions

Stuart Russell, Brian Milch
Questions on first-order probability and BLOG

3:00 - 4:00

Josh Tenenbaum (Massachusetts Institute of Technology)

First-order probabilistic models in human cognition

4:00 - 4:30

Break

4:30 - 5:30

Charles Kemp (Massachusetts Institute of Technology)

Learning relational theories

7:00

*Optional Evening Session - Stanley Kok, Markov Logic Networks (MLNs) and the Alchemy system
Presentation (PowerPoint)

8:00 - 9:00

Continental Breakfast

9:00 - 10:00

Stuart Geman (Brown University)

Hierarchy and Reusability in Image Analysis I

10:00 - 10:15

Break

10:15 - 11:15

Stuart Geman (Brown University)

Hierarchy and Reusability in Image Analysis II

11:15 - 11:30

Break

11:30 - 12:30

Alan Yuille (University of California, Los Angeles (UCLA))

Image Parsing

12:30 - 1:30

Lunch (on your own)

1:45 - 2:45

Breakout Session

Stuart Geman/Alan Yuille
Questions on grammars for image parsing

Stanley Kok
Questions on MLNs and Alchemy

3:00 - 4:00

Fei-Fei Li (Princeton University)

Discovering Meaning in the Visual World

4:00 - 4:30

Break

4:30 - 5:30

Dan Kersten (University of Minnesota, Twin Cities)

Human Object Perception: Bottom-up and Top-down

7:00

*Optional Evening Session: Stuart Russell, Brian Milch, Josh Tenenbaum, Charles Kemp, et al: Discussion on connections between Cognitive Science and AI.

8:00 - 9:00

Continental Breakfast

9:00 - 10:00

Mark Johnson (Brown University)

An introduction to grammars and parsing for language 1

10:00 - 10:15

Break

10:15 - 11:15

Mark Johnson (Brown University)

An introduction to grammars and parsing for language 2
Updated Presentation

11:15 - 11:30

Break

11:30 - 12:30

Roger Levy (University of California, San Diego)

Probabilistic sentence processing 1

12:30 - 1:30

Lunch (on your own)

1:45 - 2:45

Breakout Session

Roger Levy, Mark Johnson
Questions on grammars for language, and connections between human and machine language research

Noah Goodman
Probabilistic grammars for concept learning

3:00 - 4:00

Roger Levy (University of California, San Diego)

Probabilistic sentence processing 2

4:00 - 4:30

Break

4:30 - 5:30

Mark Steyvers (University of California, Irvine (UCI))

Semantic Representations with Probabilistic Topic Models

5:30 - 7:00

Reception hosted by IPAM (IPAM Building)

8:00 - 9:00

Continental Breakfast

9:00 - 10:00

Peter Dayan (University College London)

Models of conditioning

10:00 - 10:15

Break

10:15 - 11:15

John Kruschke (Indiana University)

Locally Bayesian Learning

11:15 - 11:30

Break

11:30 - 12:30

Nathaniel Daw (New York University)

Advanced conditioning: competition and graphical models

12:30 - 1:30

Lunch (on your own)

1:45 - 2:45

Breakout Session

Stuart Russell, Peter Dayan
Questions and dialog on reinforcement learning

3:00 - 4:00

Stuart Russell (University of California, Berkeley (UC Berkeley))

Reinforcement Learning

4:00 - 4:30

Break

4:30 - 5:30

Angela Yu (Princeton University)

Bayesian Decision Theory and Sequential Sampling

8:00 - 9:00

Continental Breakfast

9:00 - 10:00

Bernard Balleine (UCLA)

Neural Substrates for Conditioning

10:00 - 10:15

Break

10:15 - 11:15

Peter Dayan (University College London)

Neural representation of value, reward and expectation

11:15 - 11:30

Break

11:30 - 12:30

Nathaniel Daw (New York University)

Reinforcement learning in human neuroimaging; uncertainty-based competition

12:30 - 1:30

Lunch (on your own)

1:45 - 2:45

Breakout Session

Peter Dayan, Josh Tenenbaum, Angela Yu, & others
Neural representations of probability distributions

Jeffrey Beck (University of Rochester)
Bayesian Inference, Action Selection, and Learning with Probabilistic Population Codes
Presentation (PDF File)

3:00 - 4:00

Stuart Russell (University of California, Berkeley (UC Berkeley))

Hierarchical reinforcement learning

4:00 - 4:30

Break

4:30 - 5:30

Josh Tenenbaum (Massachusetts Institute of Technology)

Models of human action understanding and social cognition as inverse decision-making

8:00 - 9:00

Check-in & Continental Breakfast

9:00 - 10:00

Laurence Maloney (New York University)

Statistical decision theory as a framework for cognition and action.

10:00 - 10:15

Break

10:15 - 11:15

Nathaniel Daw (New York University)

Neuro- and behavioral economic analysis of human choice and learning

11:15 - 11:30

Break

11:30 - 12:30

Konrad Koerding (Northwestern University Medical School)

Using Decision Theory to understand Motor Control

12:30 - 1:30

Lunch (on your own)

1:45 - 2:45

Breakout Session

Craig Fox (UCLA)
Prospect theory and support theory
Reading: "Belief and Preference in Decision Under Uncertainty", by Craig R. Fox & Kelly E. See
Presentation (PDF File)

3:00 - 4:00

Josh Tenenbaum (Massachusetts Institute of Technology)

Rational analysis of human memory and prediction

4:00 - 4:30

Break

4:30 - 5:30

Rich Shiffrin (Indiana University)

Bayesian models of memory retrieval

8:00 - 9:00

Continental Breakfast

9:00 - 10:00

Amy Perfors (Massachusetts Institute of Technology)

Grammar induction in language

10:00 - 10:15

Break

10:15 - 11:15

Alan Yuille (University of California, Los Angeles (UCLA))

Grammer induction in vision

11:15 - 11:30

Break

11:30 - 12:15

Josh Tenenbaum (Massachusetts Institute of Technology)

Cognitive development as a computational challenge: the grammar analogy

12:15 - 1:15

Lunch (on your own)

1:30 - 2:30

Breakout Session

Opportunities for mathematicians and statisticians in cognitive science and neuroscience, with Alan Yuille, Mark Green, Yingnian Wu, Noah Goodman, others

Faculty Center

2:45 - 3:30

Charles Kemp (Massachusetts Institute of Technology)

The development of structured representations

3:30 - 3:50

Break

3:50 - 4:25

Tom Griffiths (University of California, Berkeley (UC Berkeley))

Development of Causal Theories

4:25 - 4:45

Break

4:45 - 5:30

Noah Goodman (Massachusetts Institute of Technology)

Learning to be causal

7:30

Discussion and Q & A on grammar induction and unsupervised learning for structured statistical models with Alan Yuille, Josh Tenenbaum, Noah Goodman and Tom Griffiths

Math Sci 4000A

8:00 - 9:00

Continental Breakfast

9:00 - 10:00

Konrad Koerding (Northwestern University Medical School)

Causal Inference in Motor Learning and Adaptation

10:00 - 10:15

Break

10:15 - 11:15

Ladan Shams (UCLA)

Multisensory integration-segregation as Bayesian inference

11:15 - 11:30

Break

11:30 - 12:30

Jerome Busemeyer (Indiana University)

Challenges for Bayes

12:30 - 1:30

Lunch (on your own)

1:45

Symposium (Future Challenges): Josh Tenenbaum, Tom Griffiths, Alan Yuille, Keith Holyoak, Rich Shiffrin

8:00 - 9:00

Continental Breakfast

9:00 - 10:00

Student Research Symposium