Autonomous vehicles must be comprehensively evaluated before deployed in
cities and highways. However, most existing evaluation approaches for
autonomous vehicles are static and lack adaptability, so they are usually
inefficient in generating challenging scenarios for tested vehicles. In this
paper, we propose an adaptive evaluation framework to efficiently evaluate
autonomous vehicles in adversarial environments generated by deep reinforcement
learning. Considering the multimodal nature of dangerous scenarios, we use
ensemble models to represent different local optimums for diversity. We then
utilize a nonparametric Bayesian method to cluster the adversarial policies.
The proposed method is validated in a typical lane-change scenario that
involves frequent interactions between the ego vehicle and the surrounding
vehicles. Results show that the adversarial scenarios generated by our method
significantly degrade the performance of the tested vehicles. We also
illustrate different patterns of generated adversarial environments, which can
be used to infer the weaknesses of the tested vehicles.

本文提出的自适应评估框架可以通过深度强化学习生成对自动驾驶车辆具有挑战性的敌对环境，应用了集合模型来表示不同本地最优解以及非参数贝叶斯方法来对敌对策略进行聚类，验证表明所生成的敌对环境可以明显降低被测试车辆的性能，通过此方法可以推断所测试车辆的缺陷。

自动驾驶车辆在变道情境中的对抗性评估

Adversarial Evaluation of Autonomous Vehicles in Lane-Change Scenarios

Predicated on the increasing abundance of electronic health records, we
investi- gate the problem of inferring individualized treatment effects using
observational data. Stemming from the potential outcomes model, we propose a
novel multi- task learning framework in which factual and counterfactual
outcomes are mod- eled as the outputs of a function in a vector-valued
reproducing kernel Hilbert space (vvRKHS). We develop a nonparametric Bayesian
method for learning the treatment effects using a multi-task Gaussian process
(GP) with a linear coregion- alization kernel as a prior over the vvRKHS. The
Bayesian approach allows us to compute individualized measures of confidence in
our estimates via pointwise credible intervals, which are crucial for realizing
the full potential of precision medicine. The impact of selection bias is
alleviated via a risk-based empirical Bayes method for adapting the multi-task
GP prior, which jointly minimizes the empirical error in factual outcomes and
the uncertainty in (unobserved) counter- factual outcomes. We conduct
experiments on observational datasets for an inter- ventional social program
applied to premature infants, and a left ventricular assist device applied to
cardiac patients wait-listed for a heart transplant. In both experi- ments, we
show that our method significantly outperforms the state-of-the-art.

基于电子病历的丰富性，本研究提出了一种基于多任务学习的方法来推断个性化治疗效果，使用非参数贝叶斯方法来学习治疗效果，同时提供置信度的点间可信区间，以实现精准医学，并证明该方法在早产儿干预社会计划和舒张期心脏病患者左心室辅助装置中明显优于现有技术。

使用多任务高斯过程进行个性化治疗效应的贝叶斯推断

Bayesian Inference of Individualized Treatment Effects using Multi-task  Gaussian Processes

A principled approach to characterize the hidden structure of networks is to
formulate generative models, and then infer their parameters from data. When
the desired structure is composed of modules or "communities", a suitable
choice for this task is the stochastic block model (SBM), where nodes are
divided into groups, and the placement of edges is conditioned on the group
memberships. Here, we present a nonparametric Bayesian method to infer the
modular structure of empirical networks, including the number of modules and
their hierarchical organization. We focus on a microcanonical variant of the
SBM, where the structure is imposed via hard constraints, i.e. the generated
networks are not allowed to violate the patterns imposed by the model. We show
how this simple model variation allows simultaneously for two important
improvements over more traditional inference approaches: 1. Deeper Bayesian
hierarchies, with noninformative priors replaced by sequences of priors and
hyperpriors, that not only remove limitations that seriously degrade the
inference on large networks, but also reveal structures at multiple scales; 2.
A very efficient inference algorithm that scales well not only for networks
with a large number of nodes and edges, but also with an unlimited number of
modules. We show also how this approach can be used to sample modular
hierarchies from the posterior distribution, as well as to perform model
selection. We discuss and analyze the differences between sampling from the
posterior and simply finding the single parameter estimate that maximizes it.
Furthermore, we expose a direct equivalence between our microcanonical approach
and alternative derivations based on the canonical SBM.

本文提出了一种基于无参数贝叶斯方法的微正则随机块模型，旨在同时改善深入贝叶斯层次结构的推断以及模型选择能力，以推断网络的模块结构和层次组织，并介绍了一种高效的推理算法。