Videos

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Kyunghyun Cho - Lab-in-the-loop de novo antibody design - what are we missing from machine learning? Thumbnail

Kyunghyun Cho - Lab-in-the-loop de novo antibody design - what are we missing from machine learning?

Presenter
  • Kyunghyun Cho
January 25, 2023
IPAM
Mark Tuckerman - From A to B via a synthesis of rare-event sampling and machine learning Thumbnail

Mark Tuckerman - From A to B via a synthesis of rare-event sampling and machine learning

Presenter
  • Mark Tuckerman
January 24, 2023
IPAM

Tutorial: Path recovery from signature feature representation

Presenter
  • Weixin Yang
July 8, 2021
ICERM
Foundations of Data Science: Mind the Gaps Thumbnail

Foundations of Data Science: Mind the Gaps

Presenter
  • Vasant Honavar
September 14, 2016
IMA
Locating Outliers in Large Matrices with Adaptive Compressive Sampling Thumbnail

Locating Outliers in Large Matrices with Adaptive Compressive Sampling

Presenter
  • Jarvis Haupt
December 8, 2015
IMA
Recommender Systems for Fun and Profit Thumbnail

Recommender Systems for Fun and Profit

Presenter
  • Chris Volinsky
April 13, 2011
IMA
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
Randomization, Neutrality, and Fairness: "Bringing Causality into Fairness: Application to Pretrial Public Safety Assessment" Thumbnail

Randomization, Neutrality, and Fairness: "Bringing Causality into Fairness: Application to Pretrial Public Safety Assessment"

Presenter
  • Kosuke Imai
October 24, 2023
SLMath
Jens Eisert - Do quantum computers have application in machine learning & combinatorial optimization Thumbnail

Jens Eisert - Do quantum computers have application in machine learning & combinatorial optimization

Presenter
  • Jens Eisert
October 17, 2023
IPAM
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

Data science for social equality

Presenter
  • Emma Pierson
March 10, 2021
ICERM
Wasserstein Distributionally Robust Optimization: Theory and Applications in Machine Learning Thumbnail

Wasserstein Distributionally Robust Optimization: Theory and Applications in Machine Learning

Presenter
  • Daniel Kuhn
February 26, 2020
IPAM