The maximum a-posteriori (MAP) perturbation framework has emerged as a useful
approach for inference and learning in high dimensional complex models. By
maximizing a randomly perturbed potential function, MAP perturbations generate
unbiased samples from the Gibbs distribution. Unfortunately, the computational
cost of generating so many high-dimensional random variables can be
prohibitive. More efficient algorithms use sequential sampling strategies based
on the expected value of low dimensional MAP perturbations. This paper develops
new measure concentration inequalities that bound the number of samples needed
to estimate such expected values. Applying the general result to MAP
perturbations can yield a more efficient algorithm to approximate sampling from
the Gibbs distribution. The measure concentration result is of general interest
and may be applicable to other areas involving expected estimations.

本文提出了新的度量区间不等式方法，用于估算低维度 MAP 扰动期望值所需的样本数量，通过将该通用结果应用于 MAP 扰动，可以产生更有效的算法以从 Gibbs 分布中近似采样。

关于随机最大后验扰动度量的专业简体中文翻译

On Measure Concentration of Random Maximum A-Posteriori Perturbations

RockIt is a maximum a-posteriori (MAP) query engine for statistical
relational models. MAP inference in graphical models is an optimization problem
which can be compiled to integer linear programs (ILPs). We describe several
advances in translating MAP queries to ILP instances and present the novel
meta-algorithm cutting plane aggregation (CPA). CPA exploits local
context-specific symmetries and bundles up sets of linear constraints. The
resulting counting constraints lead to more compact ILPs and make the symmetry
of the ground model more explicit to state-of-the-art ILP solvers. Moreover,
RockIt parallelizes most parts of the MAP inference pipeline taking advantage
of ubiquitous shared-memory multi-core architectures.
We report on extensive experiments with Markov logic network (MLN) benchmarks
showing that RockIt outperforms the state-of-the-art systems Alchemy, Markov
TheBeast, and Tuffy both in terms of efficiency and quality of results.

RockIt 是一种基于最大后验查询引擎的查询引擎，通过整数线性规划 (ILPs) 的编译实现 MAP 推理，并使用一种名为 cutting plane aggregation (CPA) 的新型元算法。它还利用共享内存多核架构并行化 MAP 推理管道的大部分部分，通过与 Alchemy、Markov TheBeast 和 Tuffy 等状态 - of-the-art 系统的大量实验表明，RockIt 在效率和结果质量方面都优于这些系统。