Estimating heterogeneous treatment effects across individuals has attracted
growing attention as a statistical tool for performing critical
decision-making. We propose a Bayesian inference framework that quantifies the
uncertainty in treatment effect estimation to support decision-making in a
relatively small sample size setting. Our proposed model places Gaussian
process priors on the nonparametric components of a semiparametric model called
a partially linear model. This model formulation has three advantages. First,
we can analytically compute the posterior distribution of a treatment effect
without relying on the computationally demanding posterior approximation.
Second, we can guarantee that the posterior distribution concentrates around
the true one as the sample size goes to infinity. Third, we can incorporate
prior knowledge about a treatment effect into the prior distribution, improving
the estimation efficiency. Our experimental results show that even in the small
sample size setting, our method can accurately estimate the heterogeneous
treatment effects and effectively quantify its estimation uncertainty.

提出了一种贝叶斯推断框架来量化治疗效果估计的不确定性，支持在相对较小的样本量条件下进行决策，该方法在小样本量设置中能够准确估计异质治疗效应并有效量化其估计不确定性。

基于高斯过程的部分线性模型实现异质治疗效应估计的不确定性量化

Uncertainty Quantification in Heterogeneous Treatment Effect Estimation  with Gaussian-Process-Based Partially Linear Model

We consider perception-based control using state estimates that are obtained
from high-dimensional sensor measurements via learning-enabled perception maps.
However, these perception maps are not perfect and result in state estimation
errors that can lead to unsafe system behavior. Stochastic sensor noise can
make matters worse and result in estimation errors that follow unknown
distributions. We propose a perception-based control framework that i)
quantifies estimation uncertainty of perception maps, and ii) integrates these
uncertainty representations into the control design. To do so, we use conformal
prediction to compute valid state estimation regions, which are sets that
contain the unknown state with high probability. We then devise a sampled-data
controller for continuous-time systems based on the notion of measurement
robust control barrier functions. Our controller uses idea from self-triggered
control and enables us to avoid using stochastic calculus. Our framework is
agnostic to the choice of the perception map, independent of the noise
distribution, and to the best of our knowledge the first to provide
probabilistic safety guarantees in such a setting. We demonstrate the
effectiveness of our proposed perception-based controller for a LiDAR-enabled
F1/10th car.

使用感知映射通过学习获取状态估计的感知控制，通过量化感知映射的估计不确定性并将这些不确定性表示整合到控制设计中，采用符合预测来计算有效的状态估计区域，从而设计采用自我触发控制思想的取样数据控制器，以避免使用随机微积分并在具有李达（LiDAR）能力的 F1 / 10th 汽车中演示了控制器的有效性。

基于共形预测的随机传感器不确定性下安全感知控制

Safe Perception-Based Control under Stochastic Sensor Uncertainty using  Conformal Prediction

Information about action costs is critical for real-world AI planning
applications. Rather than rely solely on declarative action models, recent
approaches also use black-box external action cost estimators, often learned
from data, that are applied during the planning phase. These, however, can be
computationally expensive, and produce uncertain values. In this paper we
suggest a generalization of deterministic planning with action costs that
allows selecting between multiple estimators for action cost, to balance
computation time against bounded estimation uncertainty. This enables a much
richer -- and correspondingly more realistic -- problem representation.
Importantly, it allows planners to bound plan accuracy, thereby increasing
reliability, while reducing unnecessary computational burden, which is critical
for scaling to large problems. We introduce a search algorithm, generalizing
$A^*$, that solves such planning problems, and additional algorithmic
extensions. In addition to theoretical guarantees, extensive experiments show
considerable savings in runtime compared to alternatives.

本文提出了一种带有多个估计器的确定性计划泛化模型，以平衡计算时间和有界估计不确定性，这可以增加计划的准确性，从而增加可靠性，并减少不必要的计算负担，进而应用于大规模问题的研究。通过广泛的实验证明，该算法可以较之其他方法在运行时间上实现显著的节省。