Videos

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Validation and Reproducibility by Geometry, for Unsupervised Learning Thumbnail

Validation and Reproducibility by Geometry, for Unsupervised Learning

Presenter
  • Marina Meila
October 31, 2019
IPAM
Deep Learning: Triangle Machine Learning Day - What You Didn’t Learn About Machine Learning in School, Wayne Thompson Thumbnail

Deep Learning: Triangle Machine Learning Day - What You Didn’t Learn About Machine Learning in School, Wayne Thompson

September 20, 2019
SAMSI
Deep Learning: Triangle Machine Learning Day - Biomedical Image Understanding and EHRs at LifeOmic: Harnessing the Power of the Cloud, Matthew Phillips Thumbnail

Deep Learning: Triangle Machine Learning Day - Biomedical Image Understanding and EHRs at LifeOmic: Harnessing the Power of the Cloud, Matthew Phillips

September 20, 2019
SAMSI
Special Guest Lecture: Interpretable Machine Learning, Cynthia Rudin Thumbnail

Special Guest Lecture: Interpretable Machine Learning, Cynthia Rudin

August 28, 2019
SAMSI
Topological transitions (and symmetries) of input representations in Deep Learning Thumbnail

Topological transitions (and symmetries) of input representations in Deep Learning

Presenter
  • Naftali Tishby
May 1, 2019
IPAM

Machine Learning: Dynamical, Economic and Stochastic Perspectives

Presenter
  • Michael Jordan
January 23, 2019
MBI
Deep Learning-Based Numerical Methods for High-Dimensional Stochastic Control Problems and Parabolic PDEs Thumbnail

Deep Learning-Based Numerical Methods for High-Dimensional Stochastic Control Problems and Parabolic PDEs

Presenter
  • Jiequn Han
May 9, 2018
IMA
Maximin-Projection Learning for Optimal Treatment Decision with Heterogeneous Data Thumbnail

Maximin-Projection Learning for Optimal Treatment Decision with Heterogeneous Data

Presenter
  • Wenbin Lu
September 15, 2017
IMA

Using Cover-Trees and Friends for Machine Learning with the CDER algorithm

Presenter
  • Abraham Smith
May 2, 2017
IMA
Predicting Properties and Electronic Structure of Inorganic Materials with Machine Learning Thumbnail

Predicting Properties and Electronic Structure of Inorganic Materials with Machine Learning

Presenter
  • Olexandr Isayev
November 15, 2016
IPAM
Using machine learning to map the structure and predict the properties of materials and molecules Thumbnail

Using machine learning to map the structure and predict the properties of materials and molecules

Presenter
  • Michele Ceriotti
September 27, 2016
IPAM
Polynomial-time tensor decompositions via sum-of-squares Thumbnail

Polynomial-time tensor decompositions via sum-of-squares

Presenter
  • Tengyu Ma
March 21, 2016
IAS