Workshop III: Mathematical Foundations and Algorithms for Tensor Computations

May 3 - 6, 2021

Schedule

All times in this Schedule are Pacific Time (PT)

Monday, May 3, 2021

8:00 - 8:30
Alex Wein (New York University)

The Kikuchi Hierarchy and Tensor PCA
PDF Presentation

 
8:55 - 9:25
9:50 - 10:20
Kaie Kubias (Aalto University)

Rank-one tensor completion
PDF Presentation

 
10:45 - 11:15
Mateusz Michalek (Universität Konstanz)

Algebraic methods to construct tensors
PDF Presentation

 

Tuesday, May 4, 2021

8:00 - 8:30
Nick Vannieuwenhoven (KU Leuven )

Sensitivity of tensor decompositions
PDF Presentation

 
8:55 - 9:25
9:50 - 10:20
Anh-Huy Phan (Skolkovo Institute of Science and Technology)

Chain Tensor Network: Instability and how to deal with it
PDF Presentation

 
10:45 - 11:15
Venera Khoromskaia (Max-Planck-Institut für Mathematik in den Naturwissenschaften)

Tensor numerical modeling of the collective electrostatic potentials in many-particle systems
PDF Presentation

 
11:40 - 12:10
Bart Vandereycken (Université de Genève)

Algorithms for dynamical low-rank approximation
PDF Presentation


Wednesday, May 5, 2021

8:00 - 8:30
8:55 - 9:25
David Perez-Garcia (Universidad Complutense de Madrid)

Spectral gap in PEPS
PDF Presentation

 
9:50 - 10:20
Yang Qi (Institut National de Recherche en Informatique et Automatique (INRIA))

Inner and outer approximations of tropical polytopes and their applications in tropical tensors
PDF Presentation

 
10:45 - 11:15
Maksim Rakhuba (Higher School of Economics)

Quantized tensor decompositions: challenges and applications

11:40 - 12:10
Elina Robeva (University of British Columbia)

Hidden Variables in Linear Non-Gaussian Causal Models
PDF Presentation

 

Thursday, May 6, 2021

8:00 - 8:30
8:55 - 9:25
9:50 - 10:20
Jiawang Nie (University of California, San Diego (UCSD))

Learning Diagonal Gaussian Mixture Models and Incomplete Tensor Decompositions
PDF Presentation

 
10:45 - 11:15
Aravindan Vijayaraghavan (Northwestern University)

Smoothed Analysis for Tensor Decompositions and Unsupervised Learning
PDF Presentation