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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Reminder: talk in 30 min!
On Wed, Nov 22, 2017 at 4:39 PM Michael Noukhovitch mnoukhov@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
lisa_seminaires@iro.umontreal.ca
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Michael Noukhovitch