[Lisa_seminaires] [Tea Talk] Sergey Plis (MRN) Fri Nov 24, 10:30AM, AA6214

Michael Noukhovitch mnoukhov at gmail.com
Mer 22 Nov 16:39:57 EST 2017


This week we have TWO tea talks (what a treat!) Apart from Martin
Arjovsky's talk on Thursday, we also have the director of machine learning
at the Mind Research Network, *Sergey Plis,* giving a talk on *Friday Nov
24* at* 10:30AM* in room *AA6214*.

Hope this gives you cause to come, there's lots to learn!
Michael

*KEYWORDS *neuroscience, brain imaging, bayesian networks, structure
learning

*TITLE *Causal learning in complex time series datasets

*ABSTRACT*
Cortical neurons form coherent functional networks that are surprisingly
stable across subjects and conditions. Together these networks comprise a
set of functional units of the brain. Understanding their interactions can
lead to better understanding of brain’s function and dysfunction due to
disruption of the interaction structure. The most common way of assessing
this structure are cross-correlation matrices (usually referred to as
functional connectivity) but their drawbacks make us turn to modelling
interactions via the directed graph of a Bayesian network (effective
connectivity). Various brain imaging modalities contain different and
arguably complementary information about interactions of functional
network. Our goal is to bring together multimodal information to improve
effective connectivity estimates.

In particular, I focus on the problem of finding a common denominator for
causal structures learned from time series at different time scales. I will
demonstrate 1) a general theory which explains the effects of undersampling
on apparent causal structure in terms of the true structure at the causal
time scale; 2) a forward algorithm that computes a graph structure at any
given undersampling rate; and 3) some inverse algorithms to compute all of
the candidate graphs that could have generated the given undersampled
structure.

Although the problem is inspired by the questions from brain imaging, all
time series models that are assessing structural relations are affected by
the phenomena that I will describe. This includes Dynamic Bayesian Networks
on the causal learning side and such models as vector autoregressive (VAR),
structured VAR, Granger causality.

*BASED ON*
Cook, John W., David Danks, and Sergey M. Plis. (2017) "Learning Dynamic
Structure from Undersampled Data
<https://www.cs.purdue.edu/homes/eb/causal-uai17/papers/7.pdf>." UAI
Causality Workshop

A constraint optimization approach to causal discovery from subsampled time
series data
<https://scholar.google.ca/scholar?oi=bibs&cluster=5464303113063724661&btnI=1&hl=en>
A Hyttinen, S Plis, M Järvisalo, F Eberhardt, D Danks - International
Journal of Approximate Reasoning, 2017

Danks, D., & Plis, S. M. (2017). Amalgamating evidence of dynamics.
Synthese. [PDF
<http://www.andrew.cmu.edu/user/ddanks/papers/Amalgamation-Synthese-Prepub.pdf>
]

Plis, S., Danks, D., Freeman, C., & Calhoun, V. (2015). Rate-agnostic
(causal) structure learning. In Advances in neural information processing
systems 28 (pp. 3303-3311). La Jolla, CA: The NIPS Foundation. (First two
authors contributed equally.) [PDF
<http://www.andrew.cmu.edu/user/ddanks/papers/RASL-NIPS-final.pdf>]

Plis, S., Danks, D., & Yang, J. (2015). Mesochronal structure learning. In
M. Meila & T. Heskes (Eds.), Uncertainty in artificial intelligence 31
(UAI-2015) (pp. 702-711). Corvallis, OR: AUAI Press. (First two authors
contributed equally.) [PDF
<http://www.andrew.cmu.edu/user/ddanks/papers/MSL-UAI15-final.pdf>]

Danks, D., & Plis, S. (2014). Learning causal structure from undersampled
time series. In JMLR: Workshop and Conference Proceedings. (First two
authors contributed equally.) [PDF
<http://www.andrew.cmu.edu/user/ddanks/papers/DanksPlis-Final.pdf>]


*BIO*Dr. Sergey Plis is a Director of Machine Learning at the Mind Research
Network. His research interests lie in developing novel and applying
existing techniques and approaches to analyzing large scale datasets in
multimodal brain imaging and other domains. He develops tools that fall
within the fields of machine learning and data science. One key goal is to
take advantage of the strengths of imaging modalities and infer structure
and patterns that are hard to obtain non-invasively and/or that are
unavailable for direct observation. In the long term this amounts to
developing methods capable of revealing mechanisms used by the brain for
forming task specific transient interaction networks and their
cognition-inducing interactions via multimodal fusion at features and
interaction levels. Ongoing work is focused on inferring multimodal
probabilistic and causal descriptions of these function-induced networks
based on fusion of fast and slow imaging modalities. This includes feature
estimation via deep learning-based pattern recognition and learning causal
graphical models. Dr. Plis is also the R&D director at a young machine
learning startup reason8.ai
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