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Mini Symposium
Causality and Time Series Analysis
Florin Popescu · Isabelle Guyon · Guido Nolte

Thu Dec 10 01:30 PM -- 04:30 PM (PST) @ Regency A
Event URL: http://clopinet.com/isabelle/Projects/NIPS2009/ »

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

Author Information

Florin Popescu (Fraunhofer FIRST)
Isabelle Guyon (Google and ChaLearn)

Isabelle Guyon recently joined Google Brain as a research scientist. She is also professor of artificial intelligence at Université Paris-Saclay (Orsay). Her areas of expertise include computer vision, bioinformatics, and power systems. She is best known for being a co-inventor of Support Vector Machines. Her recent interests are in automated machine learning, meta-learning, and data-centric AI. She has been a strong promoter of challenges and benchmarks, and is president of ChaLearn, a non-profit dedicated to organizing machine learning challenges. She is community lead of Codalab competitions, a challenge platform used both in academia and industry. She co-organized the “Challenges in Machine Learning Workshop” @ NeurIPS between 2014 and 2019, launched the "NeurIPS challenge track" in 2017 while she was general chair, and pushed the creation of the "NeurIPS datasets and benchmark track" in 2021, as a NeurIPS board member.

Guido Nolte

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