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Found 5 papers, 1 papers with code
Simple Binary Hypothesis Testing under Local Differential Privacy and Communication Constraints
For the sample complexity of simple hypothesis testing under pure LDP constraints, we establish instance-optimal bounds for distributions with binary support; minimax-optimal bounds for general distributions; and (approximately) instance-optimal, computationally efficient algorithms for general distributions.
An Entropy-Based Model for Hierarchical Learning
In the framework of supervised learning, a necessity for a computer to learn from data accurately and efficiently is to be provided with auxiliary information about the data distribution and target function through the learning model.
Maximum Multiscale Entropy and Neural Network Regularization
For different entropies and arbitrary scale transformations, it is shown that the distribution maximizing a multiscale entropy is characterized by a procedure which has an analogy to the renormalization group procedure in statistical physics.
Chaining Meets Chain Rule: Multilevel Entropic Regularization and Training of Neural Nets
The bounds are obtained by introducing the notion of generated hierarchical coverings of neural nets and by using the technique of chaining mutual information introduced in Asadi et al. NeurIPS'18.
Chaining Mutual Information and Tightening Generalization Bounds
Two important difficulties are (i) exploiting the dependencies between the hypotheses, (ii) exploiting the dependence between the algorithm's input and output.
