Videos

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Learning models: connections between boosting, hard-core distributions, dense models, GAN, and regularity II Thumbnail

Learning models: connections between boosting, hard-core distributions, dense models, GAN, and regularity II

Presenter
  • Russell Impagliazzo
November 14, 2017
IAS
Learning models: connections between boosting, hard-core distributions, dense models, GAN, and regularity I Thumbnail

Learning models: connections between boosting, hard-core distributions, dense models, GAN, and regularity I

Presenter
  • Russell Impagliazzo
November 13, 2017
IAS

From Large Language Models to Reasoning Language Models - Three Eras in The Age of Computation

Presenter
  • Torsten Hoefler
September 13, 2025
ICERM
Addressing Complexity in Computational Catalyst Design with Surrogate Models Thumbnail

Addressing Complexity in Computational Catalyst Design with Surrogate Models

Presenter
  • Zachary Ulissi
October 31, 2017
IPAM
Computational Experiments in Coarse-graining Atomistic Simulations Thumbnail

Computational Experiments in Coarse-graining Atomistic Simulations

Presenter
  • Yannis Kevrekidis
January 15, 2008
IMA
Michele Ceriotti - Machine learning for atomic-scale modeling - potentials and beyond - IPAM at UCLA Thumbnail

Michele Ceriotti - Machine learning for atomic-scale modeling - potentials and beyond - IPAM at UCLA

Presenter
  • Michele Ceriotti
May 3, 2023
IPAM
Machine-learning for materials and physics discovery through symbolic regression and kernel methods Thumbnail

Machine-learning for materials and physics discovery through symbolic regression and kernel methods

Presenter
  • Richard Hennig
September 26, 2019
IPAM
Rongjie Lai - Unsupervised In-context Operator Learning for Mean Field Games - IPAM at UCLA Thumbnail

Rongjie Lai - Unsupervised In-context Operator Learning for Mean Field Games - IPAM at UCLA

Presenter
  • Rongjie Lai
July 15, 2025
IPAM

Reduced label complexity for tight linear regression

Presenter
  • Alex Gittens
June 29, 2023
ICERM
Samuel Blau - High-Throughput DFT and Monte Carlo for Reaction Networks and Machine Learning Thumbnail

Samuel Blau - High-Throughput DFT and Monte Carlo for Reaction Networks and Machine Learning

Presenter
  • Samuel Blau
May 1, 2023
IPAM
Michele Ceriotti - Physical insights from atomic-scale machine learning - IPAM at UCLA Thumbnail

Michele Ceriotti - Physical insights from atomic-scale machine learning - IPAM at UCLA

Presenter
  • Michele Ceriotti
January 11, 2023
IPAM
Abhineet Agarwal - Understanding and overcoming the statistical limitations of decision trees Thumbnail

Abhineet Agarwal - Understanding and overcoming the statistical limitations of decision trees

Presenter
  • Abhineet Agarwal
February 27, 2024
IPAM