Videos

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Combinatorial Invariance: a Case Study of Pure Math / Machine Learning Interaction Thumbnail

Combinatorial Invariance: a Case Study of Pure Math / Machine Learning Interaction

Presenter
  • Geordie Williamson
March 28, 2022
IAS
Domain Adaptation Under Structural Causal Models Thumbnail

Domain Adaptation Under Structural Causal Models

Presenter
  • Yuansi Chen
March 7, 2022
SLMath
Establishing trust in decisions made from data: Physics-informed machine-learning models with computable generalization bounds Thumbnail

Establishing trust in decisions made from data: Physics-informed machine-learning models with computable generalization bounds

Presenter
  • Benjamin Peherstorfer
March 2, 2022
IMSI
Non-astrophysical transients in LIGO detectors: help with machine learning techniques Thumbnail

Non-astrophysical transients in LIGO detectors: help with machine learning techniques

Presenter
  • Gabriela González
November 29, 2021
IPAM
Learning algorithms for Mean Field Games Thumbnail

Learning algorithms for Mean Field Games

Presenter
  • Romuald Elie
November 17, 2021
IMSI
Recent Advances in Topology-Based Graph Classification Thumbnail

Recent Advances in Topology-Based Graph Classification

Presenter
  • Bastian Rieck
April 30, 2021
IMSI

Breaking into a Deep Learning box

Presenter
  • Ivan Tyukin
April 10, 2021
ICERM
A rigorous and robust quantum speed-up in supervised machine learning Thumbnail

A rigorous and robust quantum speed-up in supervised machine learning

Presenter
  • Kristan Temme
February 9, 2021
IPAM
Dealing with COVID-19 in Theory and Practice: Session II: Data Science I Thumbnail

Dealing with COVID-19 in Theory and Practice: Session II: Data Science I

Presenters
  • Mihaela van der Schaar
  • Michael Jordan
October 29, 2020
IMSI
Secure Learning in Adversarial Environments Thumbnail

Secure Learning in Adversarial Environments

Presenter
  • Bo Li
October 6, 2020
IPAM
Learning with Few Labeled Data Thumbnail

Learning with Few Labeled Data

Presenter
  • Pratik Chaudhari
October 6, 2020
IPAM
Meta-Learning: Why It’s Hard and What We Can Do Thumbnail

Meta-Learning: Why It’s Hard and What We Can Do

Presenter
  • Ke Li
April 9, 2020
IAS