NIPS 2010 Workshop on Modeling Human Communication Dynamics

NIPS Workshop on Modeling Human Communication Dynamics Friday, December 10th,
2010 Whistler, British Columbia, Canada


Face-to-face communication is a highly interactive process in which the participants mutually
exchange and interpret verbal and nonverbal messages. Both the interpersonal dynamics and
the dynamic interactions among an individual’s perceptual, cognitive, and motor processes are
swift and complex. How people accomplish these feats of coordination is a question of great
scientific interest. Models of human communication dynamics also have much potential
practical value, for applications including the understanding of communications problems
such as autism and the creation of socially intelligent robots able to recognize, predict, and
analyze verbal and nonverbal behaviors in real-time interaction with humans.

Modeling human communicative dynamics brings exciting new problems and challenges to
the NIPS community. The first goal of this workshop is to raise awareness in the machine
learning community of these problems, including some applications needs, the special
properties of these input streams, and the modeling challenges. The second goal is to
exchange information about methods, techniques, and algorithms suitable for modeling
human communication dynamics. After the workshop, depending on interest, we may
arrange to publish full-paper versions of selected submissions, possibly as a volume in the
JMLR Workshop and Conference papers series.


We invite submissions of short high-quality papers describing research on Human
Communication Dynamics and related topics. Suitable themes include, but are not limited to:

* Modeling methods robust to semi-synchronized streams (gestural,
lexical, prosodic, etc.)
* Learning methods robust to the highly variable response lags seen in
human interaction
* Coupled models for the explicit simultaneous modeling of more than
one participant
* Ways to combine symbolic (lexical) and non-symbolic information
* Learning of models that are valuable for both behavior recognition
and behavior synthesis
* Algorithms robust to training data with incomplete or noisy labels
* Feature engineering
* Online learning and adaptation
* Models of moment-by-moment human interaction that can also work for
longer time scales
* Failures and problems observed when applying existing methods
* Insights from experimental or other studies of human communication
* Concrete applications

Invited speakers

* Janet Bavelas (University of Victoria)
* Marian Stewart Bartlett (University of California, San Diego)
* Jeff Bilmes (University of Washington)
* Dan Bohus (Microsoft Research)
* Justine Cassell (Carnegie Mellon University)
* Noah D. Goodman (Stanford University)

Submission guidelines

Submissions should be written as extended abstracts, no longer than
4 pages in the NIPS latex style. NIPS style files and formatting instructions can be found at
(we will not enforce the double blind rule). Work that was recently published or presented
elsewhere is allowed, provided that the extended abstract mentions this explicitly; work
earlier presented at non-ML venues is especially encouraged. Please send your submission by
email to by October 18th, 2010 at 11:59pm PDT.

Important dates

Submission deadline (extended): October 18th, 2010, 11:59pm PDT Notification of
acceptance: November 7th, 2010
Workshop: December 10th, 2010


Louis-Philippe Morency, University of Southern California Daniel Gatica-Perez, Idiap
Research Institute Nigel Ward, University of Texas, El Paso

Sponsored by the PASCAL 2 European Network of Excellence on Pattern Analysis,
Statistical Modeling, and Computational Learning