This symposium
addresses
a topic that has spurred vigorous scientific debate of late in the
fields
of neuroscience and machine learning: causality in time-series data. In
neuroscience,
causal inference in brain signal activity (EEG, MEG, fMRI, etc.) is
challenged
by relatively rough prior
knowledge of brain connectivity and by sensor limitations
(mixing of sources). On the machine learning side, as the Causality workshop last year’s NIPS conference has
evidenced
for static (non-time series) data, there are issues of whether or not
graphical
models (directed acyclic graphs) pioneered by Judea Pearl, Peter
Spirtes,
and others can reliably provide a cornerstone of causal inference,
whereas
in neuroscience there are issues of whether Granger type causality
inference
is appropriate given the source mixing problem, traditionally addressed
by
ICA methods. Further topics, yet to be fully explored, are
non-linearity,
non-Gaussianity and full causal graph inference in high-dimensional
time
series data. Many ideas in causality research have been developed by
and
are of direct interest and relevance to researchers from fields beyond
ML
and neuroscience: economics (i.e. the Nobel Prize winning work of the
late
Clive Granger, which we will pay tribute to), process and controls
engineering,
sociology, etc. Despite the long-standing challenges of time-series
causality,
both theoretical and computational, the recent emergence of cornerstone
developments and efficient computational learning methods all point
to
the likely growth of activity in this seminal topic.
Along with the stimulating discussion of
recent
research on time-series causality, we will present and highlight
time-series
datasets added to the Causality Workbench, which have grown out of last
year’s
Causality challenge and NIPS workshop, some of
which
are neuroscience related.
Mini Symposium
Thurday, December 10, 2009
Hyatt Regency, Vancouver, Canada
Session 1.
1.30 pm - Welcome and
introduction
1.35 pm - Halbert White
and Xun Lu.
Granger
causality and dynamic structural systems [Abstract][Slides].
2:15 pm - Florin Popescu and Guido Nolte. Time
series
causality inference using the Phase Slope Index [Abstract][Slides].
2:40 pm - Coffee break
Session 2.
3:00 pm - Alard Roebroeck and Rainer Goebel. Granger
causality in brain connectivity studies using functional
Magnetic Resonance Imaging (fMRI) data [Abstract][Slides].
3:25 pm - Alessio Moneta
3:50 pm - Isabelle Guyon. Open-access datasets for time series causality discovery validation [Abstract][Slides].
Links to related workshops/competitions
NIPS 2008 causality workshop: objectives and assessment. The second challenge in causality organized by the causality workbench.
WCCI 2008
causation
and prediction challenge. A first activity of the causality
workbench.
NIPS 2006 workshop on
causality
and feature selection. The ancestor of this workshop.
IJCNN 2007 Agnostic learning
vs.
Prior knowledge challenge. “When everything
fails, ask for
additional domain knowledge” is the current motto of machine learning.
Therefore,
assessing the real added value of prior/domain knowledge is a both deep
and
practical question.The participants competed in two track: the “prior
knowledge
track” for which they had access to the raw data and information about
the
data, and the “agnostic learning track” for which they had access to
preprocessed
data with no knowledge of the identity of the features.
WCCI 2006 performance prediction challenge. “How
good
are you at predicting how good you are? 145 participants tried to
answer
that question. Cross-validation came very strong. Can you do better?
Measure
yourself against the winners by participating to the model selection
game.
NIPS
2003 workshop on feature extraction and feature selection challenge.
We organized a competition on five data sets in which hundreds of
entries
were made. The web site of the challenge is still available for post
challenge
submissions. Measure yourself against the winners! See the book we published with a CD containing the
datasets,
tutorials, papers on s.o.a. methods.
Pascal
challenges: The Pascal network is sponsoring several challenges in
Machine
learning.
Data mining competitions:
A list of data mining competitions maintained by KDnuggets, including
the
well known KDD cup.
List
of data sets for machine learning:
A rather comprehensive list maintained by MLnet.
UCI machine
learning
repository: A great collection of datasets for machine learning
research.
DELVE: A platform
developed
at
CAMDA
Critical Assessment of Microarray Data Analysis, an annual conference
on
gene expression microarray data analysis. This conference includes a
context
with emphasis on gene selection, a special case of feature selection.
ICDAR
International Conference on Document Analysis and Recognition, a
bi-annual
conference proposing a contest in printed text recognition. Feature
extraction/selection
is a key component to win such a contest.
TREC
Text Retrieval conference, organized every year by NIST. The conference
is organized around the result of a competition. Past winners have had
to
address feature extraction/selection effectively.
ICPR
In conjunction with the International Conference on Pattern
Recognition,
ICPR 2004, a face recognition contest is being organized.
CASP
An important competition in protein structure prediction called
Critical
Assessment of
Techniques for Protein Structure Prediction.
Workshop organisors: Florin C. Popescu and Guido Nolte (Fraunhofer FIRST, Germany) and Isabelle Guyon (Clopinet, USA).
Advisors:
Luiz Baccala (Escola Politecnica da
Universidade de
Sao Paulo, Brazil), Katarina Blinowska (University of
Warsaw,
Poland), Alessio Moneta (Max Planck Institute of Economics,
Germany), Mischa Rosenblum (Potsdam University,
Germany), Bjoern
Schelter (Freiburg Center for Data Analysis and Modeling,
Germany),
Pedro Valdes-Sosa (Neurosciences Center of Cuba).
Help:
causality @ clopinet . com
