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Unexpected Test Losses from Generalization Theory

Offered By: Simons Institute via YouTube

Tags

Machine Learning Courses Neural Networks Courses Overfitting Courses Model Evaluation Courses Bias-Variance Tradeoff Courses

Course Description

Overview

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Explore the intriguing topic of unexpected test losses in generalization theory with Frederic Koehler from the University of Chicago in this hour-long lecture. Delve into emerging generalization settings and gain insights into the challenges and complexities of predicting model performance across different domains. Examine the factors that contribute to unexpected outcomes in test scenarios and learn about cutting-edge research in this critical area of machine learning and artificial intelligence.

Syllabus

Unexpected Test Losses from Generalization Theory?


Taught by

Simons Institute

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