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Found 5 papers, 1 papers with code
Optimal Sampling Density for Nonparametric Regression
Assuming that a small number of initial samples are available, we derive the optimal training density that minimizes the generalization error of local polynomial smoothing (LPS) with its kernel bandwidth tuned locally: We adopt the mean integrated squared error (MISE) as a generalization criterion, and use the asymptotic behavior of the MISE as well as the locally optimal bandwidths (LOB) - the bandwidth function that minimizes MISE in the asymptotic limit.
Local Function Complexity for Active Learning via Mixture of Gaussian Processes
Inhomogeneities in real-world data, e. g., due to changes in the observation noise level or variations in the structural complexity of the source function, pose a unique set of challenges for statistical inference.
Sharing Hash Codes for Multiple Purposes
In this paper, we propose {multiple purpose LSH (mp-LSH) which shares the hash codes for different dissimilarities.
Validity of time reversal for testing Granger causality
Inferring causal interactions from observed data is a challenging problem, especially in the presence of measurement noise.
Fast Cross-Validation via Sequential Testing
With the increasing size of today's data sets, finding the right parameter configuration in model selection via cross-validation can be an extremely time-consuming task.
