This event is part of Algebraic Statistics and Our Changing World View Details

Bayesian Statistics and Statistical Learning

New Directions in Algebraic Statistics

Description

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This workshop will explore new directions for algebraic statistics in the realm of Bayesian statistics and statistical learning.  The considered topics will cover a broad range of problems from modern statistics and machine learning for which underlying algebraic structure provides a common theme.  Topics of particular interest are singular models and variational inference, invariance and equivariance in statistics and machine learning, and new interdisciplinary connections between computational algebraic geometry and machine learning.  

Organizers

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M D
Mathias Drton Technical University of Munich
​ H
​Jonathan Hauenstein University of Notre Dame
L L
Lek-Heng Lim University of Chicago
D P
Debdeep Pati Texas A&M University

Speakers

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B A
Byron Aragam University of Chicago
E C
Emma Cobian University of Notre Dame
J d L
Jesus de Loera University of California, Davis
N D
Nadav Dyem Technion – Israel Institute of Technology
K H
Kathryn Heal Google
P H
Peter Hoff Duke University
V K
Vishesh Karwa Temple University
J K
Joe Kileel University of Texas, Austin
K K
Kathlén Kohn KTH Royal Institute of Technology
R K
Risi Kondor University of Chicago
S L
Shaowei Lin Topos Institute
A M
Andrew McCormack Duke University
G M
Guido Montufar University of California, Los Angeles (UCLA)
L N
Long Nguyen University of Michigan
L O
Luke Oeding Auburn University
S P
Sean Plummer University of Arkansas
J R
Judith Rousseau University of Oxford
K W
Kazuho Watanabe Toyohashi University of Technology
S W
Sumio Watanabe Tokyo Institute of Technology
H X
Han Xiao Rutgers University

Registration

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