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An Information-Theoretic Perspective on Overfitting and Underfitting
We present an information-theoretic framework for understanding overfitting and underfitting in machine learning and prove the formal undecidability of determining whether an arbitrary classification algorithm will overfit a dataset.
The Labeling Distribution Matrix (LDM): A Tool for Estimating Machine Learning Algorithm Capacity
Algorithm performance in supervised learning is a combination of memorization, generalization, and luck.
The Bias-Expressivity Trade-off
Learning algorithms need bias to generalize and perform better than random guessing.
The Futility of Bias-Free Learning and Search
For a given degree of bias towards a fixed target, we show that the proportion of favorable information resources is strictly bounded from above.
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