2 papers
cs.LG2023
Wild-Tab: A Benchmark For Out-Of-Distribution Generalization In Tabular Regression
Sergey Kolesnikov
Out-of-Distribution (OOD) generalization, a cornerstone for building robust machine learning models capable of handling data diverging from the training set's distribution, is an o…
cs.LG2023
Identity Curvature Laplace Approximation for Improved Out-of-Distribution Detection
Maksim Zhdanov, Stanislav Dereka, Sergey Kolesnikov
Uncertainty estimation is crucial in safety-critical applications, where robust out-of-distribution (OOD) detection is essential. Traditional Bayesian methods, though effective, ar…