1 code implementation • 1 Mar 2021 • Jiaoyi Zhang, Yihan Gao
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.
no code implementations • ICLR 2019 • Yihan Gao, Chao Zhang, Jian Peng, Aditya Parameswaran
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.
no code implementations • 9 Jun 2015 • Yihan Gao, Aditya Parameswaran, Jian Peng
We study the interpretability of conditional probability estimates for binary classification under the agnostic setting or scenario.