A Bayesian Nonparametric Perspective on Mahalanobis Distance for Out of Distribution Detection

Bayesian nonparametric methods are naturally suited to the problem of out-of-distribution (OOD) detection. However, these techniques have largely been eschewed in favor of simpler methods based on distances between pre-trained or learned embeddings of data points. Here we show a formal relationship between Bayesian nonparametric models and the relative Mahalanobis distance score (RMDS), a commonly used method for OOD detection. Building on this connection, we propose Bayesian nonparametric mixture models with hierarchical priors that generalize the RMDS. We evaluate these models on the OpenOOD detection benchmark and show that Bayesian nonparametric methods can improve upon existing OOD methods, especially in regimes where training classes differ in their covariance structure and where there are relatively few data points per class.

Randolph W. Linderman, Noah Cowan, Yiran Chen, Scott W. Linderman (2,3) __INSTITUTION_4__ Electrical, Computer Engineering Department, Duke University, Durham, NC, USA, (2) Statistics Department, Stanford University, Stanford, CA, USA, (3) The Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA) · 2025