Connectionists: Call for Posters: NIPS 2008 Workshop on Machine Learning Meets Human Learning

[jerryzhu@cs.wisc.edu]

NIPS 2008 Workshop on Machine Learning Meets Human Learning
http://pages.cs.wisc.edu/~jerryzhu/nips08.html
Whistler, Canada
December 12, 2008

Description
-----------

Can statistical machine learning theories and algorithms help explain
human learning? Broadly speaking, machine learning studies the fundamental
laws that govern all learning processes, including both artificial systems
(e.g., computers) and natural systems (e.g., humans). It has long been
understood that theories and algorithms from machine learning are relevant
to understanding aspects of human learning. For example, hierarchical
Bayesian models provide a way to understand how people could maintain
uncertainty at different levels of abstraction; neural networks have been
a valuable tool for psychologists as a computational model of the way
brains learn; reinforcement learning agrees well with the neural activity
of dopaminergic neurons during reward-based learning; and sparse
representations in computer vision predict well the visual features found
in the early visual cortex. Human cognition also carries potential lessons
for machine learning research, since people still learn languages,
concepts, and causal relationships from far less data than any automated
system. There is a rich opportunity to develop a general theory of
learning which covers both machines and humans, with the potential to
deepen our understanding of human cognition and to take insights from
human learning to improve machine learning systems.

This workshop will consist of invited talks and contributed posters. The
goal is to bring together the different communities that study machine
learning, cognitive science, neuroscience and educational science. First,
we seek to provide researchers with a common grounding in the study of
learning, by translating different disciplines' proprietary knowledge,
specialized methods, assumptions, goals into shared terminologies and
problem formulation. Second, we will investigate the value of advanced
machine learning theories and algorithms as computational models for
certain human learning behaviors, including, but not limited to, the role
of prior knowledge, learning from labeled and unlabeled data, learning
from active queries, and so on. Finally, we wish to explore the insights
from the cognitive study of human learning to inspire novel machine
learning theories and algorithms. It is our hope that the NIPS workshop
will provide a venue for cross-pollination of machine learning approaches
and cognitive theories of learning to spur further advances in both areas.

The 1-day workshop consists of invited talks, poster sessions, and panel
discussions.


Workshop webpage
----------------

http://pages.cs.wisc.edu/~jerryzhu/nips08.html


Call for Poster Contributions
-----------------------------

We invite poster submissions on all topics at the interface of machine
learning and human learning. Please submit a 200-word to one-page extended
abstract via email to Xiaojin Zhu (jerryzhu at cs.wisc.edu). The abstract
must be in either plain text or PDF. Please include "NIPS Workshop
Abstract" in the subject of your email.


Important Dates
---------------

    * Poster Abstract Submission Date: Oct. 10, 2008
    * Notification of Poster Acceptance: Oct. 17, 2008
    * Workshop date: December 12, 2008


Organizers
----------

    * Nathaniel Daw (New York University).
    * Tom Griffiths (Berkeley).
    * Josh Tenenbaum (MIT).
    * Xiaojin (Jerry) Zhu (University of Wisconsin-Madison).