  We study the problem of robustly estimating the posterior distribution for
the setting where observed data can be contaminated with potentially
adversarial outliers. We propose Rob-ULA, a robust variant of the Unadjusted
Langevin Algorithm (ULA), and provide a finite-sample analysis of its sampling
distribution. In particular, we show that after $T=
\tilde{\mathcal{O}}(d/\varepsilon_{\textsf{acc}})$ iterations, we can sample
from $p_T$ such that $\text{dist}(p_T, p^*) \leq \varepsilon_{\textsf{acc}} +
\tilde{\mathcal{O}}(\epsilon)$, where $\epsilon$ is the fraction of
corruptions. We corroborate our theoretical analysis with experiments on both
synthetic and real-world data sets for mean estimation, regression and binary
classification.
