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Alexander Giessing

dblp:209/4893 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2017
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
1 paper
Probabilistic and Bayesian machine learning · 100%
Interdisciplinary, comprehensive, and emerging computing
1 paper
Medical and health informatics · 100%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Machine learning › Probabilistic and Bayesian machine learning › structured models › graphical models
gaussian graphical model		 0.312017
Time-dependent spatially varying graphical models, with application to brain fMRI data analysis · NIPS 2017
Machine learning › Probabilistic and Bayesian machine learning › structured models
graphical models		 0.312017
Time-dependent spatially varying graphical models, with application to brain fMRI data analysis · NIPS 2017
Medical and health informatics › neuroimaging › neuroimaging analysis
fMRI analysis		 0.312017
Time-dependent spatially varying graphical models, with application to brain fMRI data analysis · NIPS 2017
Medical and health informatics
neuroimaging		 0.312017
Time-dependent spatially varying graphical models, with application to brain fMRI data analysis · NIPS 2017

Methods — techniques the papers use, named apart from their topics

time-varying covariance · 0.6single sample convergence · 0.6additive model · 0.6
YearPublicationVenuePosition
2017 Time-dependent spatially varying graphical models, with application to brain fMRI data analysis
abstract
In this work, we present an additive model for space-time data that splits the data into a temporally correlated component and a spatially correlated component. We model the spatially correlated portion using a time-varying Gaussian graphical model. Under assumptions on the smoothness of changes in covariance matrices, we derive strong single sample convergence results, confirming our ability to estimate meaningful graphical structures as they evolve over time. We apply our methodology to the discovery of time-varying spatial structures in human brain fMRI signals.
Kristjan Greenewald, Shuheng Zhou 0002, Alexander Giessing
 NIPS4