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Found 7 papers, 1 papers with code
Explainable AI needs formal notions of explanation correctness
This will lead to notions of explanation correctness that can be theoretically verified and objective metrics of explanation performance that can be assessed using ground-truth data.
EXACT: Towards a platform for empirically benchmarking Machine Learning model explanation methods
The evolving landscape of explainable artificial intelligence (XAI) aims to improve the interpretability of intricate machine learning (ML) models, yet faces challenges in formalisation and empirical validation, being an inherently unsupervised process.
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.
