Главная
Study mode:
on
1
Introduction
2
Demo
3
Proxy Problem
4
Correlation
5
Environments
6
Mixture coefficient
7
trivialexistent cases
8
prediction
9
environment
10
adversarial domain adaptation
11
hiring problems
12
observer self
13
supervised problem
14
conclusion
Description:
Explore the concept of causal invariance in representation learning through this NYU ECE seminar featuring Leon Bottou from Facebook AI Research. Delve into topics such as the proxy problem, correlation environments, mixture coefficients, and adversarial domain adaptation. Gain insights into practical applications like hiring problems and observer self-supervised learning. Discover how causal invariance can enhance prediction accuracy across different environments and improve the robustness of AI models.

Learning Representations Using Causal Invariance

New York University (NYU)
Add to list
0:00 / 0:00