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.
Unexpected Test Losses from Generalization Theory
Overview
Syllabus
Unexpected Test Losses from Generalization Theory?
Taught by
Simons Institute
Related Courses
-
Does Generalization Imply Accuracy on the Line? A New Look at Robust Generalization
-
Generalization from the Behavioral Perspective
-
Reconsidering Overfitting in the Age of Overparameterized Models
-
The Elusive Generalization and Easy Optimization - Part 2
-
Modern Paradigms of Generalization - The Heliocentric Model of Aristarchus
-
The Elusive Generalization and Easy Optimization in Machine Learning - Part 1
