Minimizing a submodular function given noisy oracle access
A FOCS'15 paper by Lee, Sidford, and Wong [LSW15] can be leveraged to obtain such minimization guarantees -- cf. Section 5 (specifically, Corollary 5.4) in our recent paper ([BCELR16]).


  Corollary 5.4. Let $g\colon 2^{[m]} \to \mathbb{R}$ be a submodular function. There exists an algorithm that, when given access to an approximate oracle $\mathcal{O}^{\pm}_g$ for $g$, and for input  parameters $\xi, \delta\in(0,1)$,  returns with probability at least $9/10-2\delta$ a value $\nu \in \mathbb{R}$ such that $\nu\leq \min_{S \subseteq [m]} \{g(S)\} + \xi$. 
  
  The algorithm performs $m\log\left(\frac{mM}{\xi}\right)$ calls to $\mathcal{O}^{\pm}_g$ with parameters $\frac{\xi^2}{128m^5M^2}$ and $\frac{\delta}{Cm^2\log\left(\frac{mM}{\xi} \right)}$, where $M\stackrel{\rm def}{=} \max\left\{{2}\max_{S \subseteq [m]}\{\lvert{g(S)}\rvert\},\xi/2 \right\}$ and $C&gt;0$ is an absolute constant.
  
  The running time of the algorithm is
  $$
O\!\left( m^2\cdot \Phi_g\left( \frac{\xi^2}{128m^{5}M^2}, \frac{\delta}{Cm^2\log\frac{mM}{\xi}} \right)\log{\frac{mM}{\xi}}  + m^3 \log^{O(1)}\frac{mM}{\xi}\right) \;,
$$
  where $\Phi_g$ is the running time of $\mathcal{O}^{\pm}_g$.




[LSW15] A Faster Cutting Plane Method and its Implications for Combinatorial and Convex Optimization, Yin Tat Lee, Aaron Sidford, Sam Chiu-wai Wong. FOCS'15. http://arxiv.org/abs/1508.04874

[BCELR16] Tolerant Junta Testing and the Connection to Submodular Optimization and Function Isomorphism. Eric Blais, Clément L. Canonne, Talya Eden, Amit Levi, Dana Ron. arXiv, 2016. https://arxiv.org/abs/1607.03938