Data-driven neural path planners are attracting increasing interest in the
robotics community. However, their neural network components typically come as
black boxes, obscuring their underlying decision-making processes. Their
black-box nature exposes them to the risk of being compromised via the
insertion of hidden malicious behaviors. For example, an attacker may hide
behaviors that, when triggered, hijack a delivery robot by guiding it to a
specific (albeit wrong) destination, trapping it in a predefined region, or
inducing unnecessary energy expenditure by causing the robot to repeatedly
circle a region. In this paper, we propose a novel approach to specify and
inject a range of hidden malicious behaviors, known as backdoors, into neural
path planners. Our approach provides a concise but flexible way to define these
behaviors, and we show that hidden behaviors can be triggered by slight
perturbations (e.g., inserting a tiny unnoticeable object), that can
nonetheless significantly compromise their integrity. We also discuss potential
techniques to identify these backdoors aimed at alleviating such risks. We
demonstrate our approach on both sampling-based and search-based neural path
planners.

提出了一种在神经路径规划器中注入隐藏的恶意行为（也称为后门）的新方法，并讨论了识别这些后门的潜在技术。

通过微小扰动操作神经路径规划器

Manipulating Neural Path Planners via Slight Perturbations

Hybrid models combine mechanistic ODE-based dynamics with flexible and
expressive neural network components. Such models have grown rapidly in
popularity, especially in scientific domains where such ODE-based modeling
offers important interpretability and validated causal grounding (e.g., for
counterfactual reasoning). The incorporation of mechanistic models also
provides inductive bias in standard blackbox modeling approaches, critical when
learning from small datasets or partially observed, complex systems.
Unfortunately, as hybrid models become more flexible, the causal grounding
provided by the mechanistic model can quickly be lost. We address this problem
by leveraging another common source of domain knowledge: ranking of treatment
effects for a set of interventions, even if the precise treatment effect is
unknown. We encode this information in a causal loss that we combine with the
standard predictive loss to arrive at a hybrid loss that biases our learning
towards causally valid hybrid models. We demonstrate our ability to achieve a
win-win -- state-of-the-art predictive performance and causal validity -- in
the challenging task of modeling glucose dynamics during exercise.

通过将机械化 ODE 动力学与灵活且富有表现力的神经网络组件结合起来，我们解决了混合模型中因机械化模型的灵活性而失去因果关系的问题，并在建模运动期间的血糖动力学方面实现了既具有领先预测性能又具有因果有效性的双赢。

混合方块神经 ODE 因果建模

Hybrid Square Neural ODE Causal Modeling

Combining discrete probability distributions and combinatorial optimization
problems with neural network components has numerous applications but poses
several challenges. We propose Implicit Maximum Likelihood Estimation (I-MLE),
a framework for end-to-end learning of models combining discrete exponential
family distributions and differentiable neural components. I-MLE is widely
applicable as it only requires the ability to compute the most probable states
and does not rely on smooth relaxations. The framework encompasses several
approaches such as perturbation-based implicit differentiation and recent
methods to differentiate through black-box combinatorial solvers. We introduce
a novel class of noise distributions for approximating marginals via
perturb-and-MAP. Moreover, we show that I-MLE simplifies to maximum likelihood
estimation when used in some recently studied learning settings that involve
combinatorial solvers. Experiments on several datasets suggest that I-MLE is
competitive with and often outperforms existing approaches which rely on
problem-specific relaxations.

该论文提出了一种基于离散指数族分布和可微神经组件相结合的模型的端到端学习框架 Implicit Maximum Likelihood Estimation（I-MLE），并介绍了一种新型的噪声分布 perturb-and-MAP，该框架在几个数据集上表现良好，并且与依赖于特定问题松弛的现有方法相比竞争力强。

隐式最大似然估计：通过离散指数族分布反向传播

Implicit MLE: Backpropagating Through Discrete Exponential Family  Distributions

Probabilistic programming languages (PPLs) are a powerful modeling tool, able
to represent any computable probability distribution. Unfortunately,
probabilistic program inference is often intractable, and existing PPLs mostly
rely on expensive, approximate sampling-based methods. To alleviate this
problem, one could try to learn from past inferences, so that future inferences
run faster. This strategy is known as amortized inference; it has recently been
applied to Bayesian networks and deep generative models. This paper proposes a
system for amortized inference in PPLs. In our system, amortization comes in
the form of a parameterized guide program. Guide programs have similar
structure to the original program, but can have richer data flow, including
neural network components. These networks can be optimized so that the guide
approximately samples from the posterior distribution defined by the original
program. We present a flexible interface for defining guide programs and a
stochastic gradient-based scheme for optimizing guide parameters, as well as
some preliminary results on automatically deriving guide programs. We explore
in detail the common machine learning pattern in which a 'local' model is
specified by 'global' random values and used to generate independent observed
data points; this gives rise to amortized local inference supporting global
model learning.

该论文提出了一种在概率编程语言中实现摊余推理的方法，摊余通过参数化的指南程序实现，并且可以包括神经网络组件以更好地进行采样，支持全局模型学习。