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

Michael Noukhovitch mnoukhov at gmail.com
Ven 24 Nov 10:02:32 EST 2017


Reminder: talk in 30 min!

On Wed, Nov 22, 2017 at 4:39 PM Michael Noukhovitch <mnoukhov at gmail.com>
wrote:

> 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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