Validating safety-critical autonomous systems in high-dimensional domains
such as robotics presents a significant challenge. Existing black-box
approaches based on Markov chain Monte Carlo may require an enormous number of
samples, while methods based on importance sampling often rely on simple
parametric families that may struggle to represent the distribution over
failures. We propose to sample the distribution over failures using a
conditional denoising diffusion model, which has shown success in complex
high-dimensional problems such as robotic task planning. We iteratively train a
diffusion model to produce state trajectories closer to failure. We demonstrate
the effectiveness of our approach on high-dimensional robotic validation tasks,
improving sample efficiency and mode coverage compared to existing black-box
techniques.

在高维领域中验证安全关键的自主系统（如机器人）是一个重大挑战。我们提出使用条件降噪扩散模型对故障分布进行采样，并经验证在高维机器人验证任务中相比现有的黑盒技术，能够提高采样效率和模态覆盖。

基于扩散的故障采样技术对于网络物理系统

Diffusion-Based Failure Sampling for Cyber-Physical Systems

Generative models have attracted significant interest due to their ability to
handle uncertainty by learning the inherent data distributions. However, two
prominent generative models, namely Generative Adversarial Networks (GANs) and
Variational AutoEncoders (VAEs), exhibit challenges that impede achieving
optimal performance in sequential recommendation tasks. Specifically, GANs
suffer from unstable optimization, while VAEs are prone to posterior collapse
and over-smoothed generations. The sparse and noisy nature of sequential
recommendation further exacerbates these issues. In response to these
limitations, we present a conditional denoising diffusion model, which includes
a sequence encoder, a cross-attentive denoising decoder, and a step-wise
diffuser. This approach streamlines the optimization and generation process by
dividing it into easier and tractable steps in a conditional autoregressive
manner. Furthermore, we introduce a novel optimization schema that incorporates
both cross-divergence loss and contrastive loss. This novel training schema
enables the model to generate high-quality sequence/item representations and
meanwhile precluding collapse. We conducted comprehensive experiments on four
benchmark datasets, and the superior performance achieved by our model attests
to its efficacy.

本研究提出了一种 条件去噪扩散模型 以应对生成敌对网络和变分自编码器在顺序推荐任务中的挑战，通过将过程分成易于处理的步骤来简化优化和推荐任务，同时采用新的优化模式，模型能够生成高质量的序列 / 项目表示并防止折叠