Videos

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Accelerated Optimization in the PDE Framework Thumbnail

Accelerated Optimization in the PDE Framework

Presenter
  • Anthony Yezzi
April 22, 2020
IPAM

Optimal experimental design for the quantification of model uncertainty

Presenter
  • Karen Veroy-Grepl
April 22, 2020
ICERM
The joint power of deterministic and Bayesian approaches for seismic inverse problems Thumbnail

The joint power of deterministic and Bayesian approaches for seismic inverse problems

Presenter
  • Yunan Yang
April 22, 2020
IPAM
Rank optimality of the Burer-Monteiro factorization Thumbnail

Rank optimality of the Burer-Monteiro factorization

Presenter
  • Irene Waldspurger
April 22, 2020
IPAM

Metric representations - Algorithms and Geometry

Presenter
  • Anna Gilbert
April 21, 2020
ICERM
Toward a theory of optimization for over-parameterized systems of non-linear equations: the lessons of deep learning Thumbnail

Toward a theory of optimization for over-parameterized systems of non-linear equations: the lessons of deep learning

Presenter
  • Mikhail Belkin
April 21, 2020
IPAM
Assumption-free prediction intervals for black-box regression algorithms Thumbnail

Assumption-free prediction intervals for black-box regression algorithms

Presenter
  • Aaditya Ramdas
April 21, 2020
IAS
A mean-field theory of lazy training in two-layer neural nets: entropic regularization and controlled McKean-Vlasov dynamics Thumbnail

A mean-field theory of lazy training in two-layer neural nets: entropic regularization and controlled McKean-Vlasov dynamics

Presenter
  • Maxim Raginsky
April 21, 2020
IPAM
Non-commutative optimization: theory, algorithms and applications (or, can we prove P!=NP using gradient descent) Thumbnail

Non-commutative optimization: theory, algorithms and applications (or, can we prove P!=NP using gradient descent)

Presenter
  • Avi Wigderson
April 21, 2020
IAS

Understanding and mitigating gradient flow pathologies in physics-informed neural networks

Presenter
  • Paris Perdikaris
April 21, 2020
ICERM
Active learning and experimental design - who should we test? Thumbnail

Active learning and experimental design - who should we test?

Presenter
  • Eldad Haber
April 21, 2020
IPAM
Projected Stein variational methods for high-dimensional Bayesian inversion constrained by large-scale PDEs Thumbnail

Projected Stein variational methods for high-dimensional Bayesian inversion constrained by large-scale PDEs

Presenter
  • Peng Chen
April 21, 2020
IPAM