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Information-Theoretic Analysis of Minimax Excess Risk
We consider the frequentist problem of minimax excess risk as a zero-sum game between the algorithm designer and the world.
Rate-Distortion Analysis of Minimum Excess Risk in Bayesian Learning
For the upper bound, the optimization is further constrained to use $R$ bits from the training set, a setting which relates MER to information-theoretic bounds on the generalization gap in frequentist learning.
Conditioning and Processing: Techniques to Improve Information-Theoretic Generalization Bounds
This approach provides an insight into learning algorithms by considering the mutual information between the model and the training set.
Do Compressed Representations Generalize Better?
In this paper, the constraint on the entropy $H(X)$ of the input variable $X$ is studied as a simplicity assumption.
Information Bottleneck and its Applications in Deep Learning
Information Theory (IT) has been used in Machine Learning (ML) from early days of this field.
