Videos

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Geometry of the Gaussian Graphical Model of the Cycle Thumbnail

Geometry of the Gaussian Graphical Model of the Cycle

Presenter
  • Rodica Dinu
February 13, 2025
IAS
Introductory Workshop: Probability and Statistics of Discrete Structures: Permutations from Symmetric Random Walk Thumbnail

Introductory Workshop: Probability and Statistics of Discrete Structures: Permutations from Symmetric Random Walk

Presenter
  • Peter Winkler
January 31, 2025
SLMath
Introductory Workshop: Probability and Statistics of Discrete Structures: The largest common subtree of uniform attachment trees Thumbnail

Introductory Workshop: Probability and Statistics of Discrete Structures: The largest common subtree of uniform attachment trees

Presenter
  • Miklós Rácz
January 30, 2025
SLMath
Olya Mandelshtam - compact formula for the symmetric Macdonald polynomials via the ASEP and TAZRP Thumbnail

Olya Mandelshtam - compact formula for the symmetric Macdonald polynomials via the ASEP and TAZRP

April 16, 2024
IPAM
Arvind Ayyer - The inhomogeneous multispecies PushTASEP - IPAM at UCLA Thumbnail

Arvind Ayyer - The inhomogeneous multispecies PushTASEP - IPAM at UCLA

Presenter
  • Arvind Ayyer
April 16, 2024
IPAM
Holography of the Loewner Energy Thumbnail

Holography of the Loewner Energy

Presenter
  • Yilin Wang
April 14, 2023
IAS
Langevin Dynamics of Yang-Mills in 2D and 3D Thumbnail

Langevin Dynamics of Yang-Mills in 2D and 3D

Presenter
  • Hao Shen
January 20, 2023
IAS
Optimal Weak to Strong Learning Thumbnail

Optimal Weak to Strong Learning

Presenter
  • Kasper Larsen
December 5, 2022
IAS

Lower bounds for the L^p norms of some Fourier multipliers

Presenter
  • Vjekoslav Kovač
December 2, 2022
ICERM

Braid groups and permutations of the Kazhdan-Lusztig basis

Presenter
  • Oded Yacobi
February 16, 2022
ICERM
On eigenvectors of perturbed Toeplitz matrices Thumbnail

On eigenvectors of perturbed Toeplitz matrices

Presenter
  • Ofer Zeitouni
August 23, 2021
SLMath
Why Deep Learning Works: Heavy-Tailed Random Matrix Theory as an Example of Physics Informed Machine Learning Thumbnail

Why Deep Learning Works: Heavy-Tailed Random Matrix Theory as an Example of Physics Informed Machine Learning

Presenter
  • Michael Mahoney
October 14, 2019
IPAM