Bayesian optimization has been challenged by datasets with large-scale,
high-dimensional, and non-stationary characteristics, which are common in
real-world scenarios. Recent works attempt to handle such input by applying
neural networks ahead of the classical Gaussian process to learn a latent
representation. We show that even with proper network design, such learned
representation often leads to collision in the latent space: two points with
significantly different observations collide in the learned latent space,
leading to degraded optimization performance. To address this issue, we propose
LOCo, an efficient deep Bayesian optimization framework which employs a novel
regularizer to reduce the collision in the learned latent space and encourage
the mapping from the latent space to the objective value to be Lipschitz
continuous. LOCo takes in pairs of data points and penalizes those too close in
the latent space compared to their target space distance. We provide a rigorous
theoretical justification for LOCo by inspecting the regret of this
dynamic-embedding-based Bayesian optimization algorithm, where the neural
network is iteratively retrained with the regularizer. Our empirical results
demonstrate the effectiveness of LOCo on several synthetic and real-world
benchmark Bayesian optimization tasks.

提出 LOCo，一种有效的深度贝叶斯优化框架，其采用一种新的正则化方法来降低学习到的潜在空间中的冲突，并促进从潜在空间到目标值的映射具有 Lipschitz 连续性，通过将数据点分成不同距离空间，来惩罚太接近的数据点，该算法的实验结果在多项绩效测试中表现出非常明显的效果。

基于无碰撞正则化的贝叶斯优化学习表示

Learning Representation for Bayesian Optimization with Collision-free Regularization

Face de-identification has become increasingly important as the image sources
are explosively growing and easily accessible. The advance of new face
recognition techniques also arises people's concern regarding the privacy
leakage. The mainstream pipelines of face de-identification are mostly based on
the k-same framework, which bears critiques of low effectiveness and poor
visual quality. In this paper, we propose a new framework called
Privacy-Protective-GAN (PP-GAN) that adapts GAN with novel verificator and
regulator modules specially designed for the face de-identification problem to
ensure generating de-identified output with retained structure similarity
according to a single input. We evaluate the proposed approach in terms of
privacy protection, utility preservation, and structure similarity. Our
approach not only outperforms existing face de-identification techniques but
also provides a practical framework of adapting GAN with priors of domain
knowledge.

提出了一种基于隐私保护 GAN（PP-GAN）的新框架，将具有新颖真实性验证器和调节器模块的 GAN 用于人脸去识别问题，以确保在保持结构相似度的情况下产生去识别输出。该方法在隐私保护、实用保护和结构相似度方面的表现优于现有的人脸去识别技术，并提供了一种通过领域知识先验来封装 GAN 框架的实用解决方案。