Counterfactual Reasoning and Learning Systems

author: Léon Bottou, Facebook
published: May 28, 2013,   recorded: May 2013,   views: 3738
Categories

Related content

Report a problem or upload files

If you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Lecture popularity: You need to login to cast your vote.
  Delicious Bibliography

Description

Using the search engine ad placement problem as an example, we explain the central role of causal inference for the design of learning systems interacting with their environments. Thanks to importance sampling techniques, data collected during randomized experiments gives precious cues to assist the designer of such learning systems and useful signals to drive learning algorithms. Thanks to a sharp distinction between the learning algorithms and the extraction of the signals that drive them, these methods can be tailored to causal models with different structures. Thanks to mathematical foundations shared with physics, these signals can describe the response of the system when equilibrium conditions are reached.

Link this page

Would you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !

Write your own review or comment:

make sure you have javascript enabled or clear this field: