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Found 3 papers, 1 papers with code
CARMI: A Cache-Aware Learned Index with a Cost-based Construction Algorithm
Specifically, we introduce entropy as a metric to quantify and characterize the effectiveness of data partitioning of tree nodes in learned indexes and propose a novel cost model, laying a new theoretical foundation for future research.
The Importance of Norm Regularization in Linear Graph Embedding: Theoretical Analysis and Empirical Demonstration
Both theoretical and empirical evidence are provided to support this argument: (a) we prove that the generalization error of these methods can be bounded by limiting the norm of vectors, regardless of the embedding dimension; (b) we show that the generalization performance of linear graph embedding methods is correlated with the norm of embedding vectors, which is small due to the early stopping of SGD and the vanishing gradients.
On the Interpretability of Conditional Probability Estimates in the Agnostic Setting
We study the interpretability of conditional probability estimates for binary classification under the agnostic setting or scenario.
