Fully probabilistic design for knowledge fusion between Bayesian filters under uniform disturbances

LEAP-CBF：具有最小努力对抗势的不确定系统安全过滤器

LEAP-CBF: A Safety Filter for Uncertain Systems with Least-Effort Adversarial Potentials

本文提出LEAP证书，量化扰动导致失败所需努力，并基于其构造鲁棒安全过滤器，通过深度强化学习构建，仿真和硬件实验验证其有效性。

Initialising Kernel Adaptive Filters via Probabilistic Inference

We present a probabilistic framework for both (i) determining the initial settings of kernel adaptive filters (KAFs) and (ii) constructing fully-adaptive KAFs whereby in addition to weights and dictionaries, kernel parameters are learnt sequentially. This is achieved by formulating the estimator as a probabilistic model and defining dedicated prior distributions over the kernel parameters, weights and dictionary, enforcing desired properties such as sparsity. The model can then be trained using a subset of data to initialise standard KAFs or updated sequentially each time a new observation bec…

Probabilistic Energy Forecasting using Quantile Regressions based on a new Nearest Neighbors Quantile Filter

Parametric quantile regressions are a useful tool for creating probabilistic energy forecasts. Nonetheless, since classical quantile regressions are trained using a non-differentiable cost function, their creation using complex data mining techniques (e.g., artificial neural networks) may be complicated. This article presents a method that uses a new nearest neighbors quantile filter to obtain quantile regressions independently of the utilized data mining technique and without the non-differentiable cost function. Thereafter, a validation of the presented method using the dataset of the Global…

基于扰动观测器控制障碍函数的安全高效强化学习

Safe and Efficient Reinforcement Learning Using Disturbance-Observer-Based Control Barrier Functions

提出一种基于扰动观测器和控制障碍函数的安全强化学习方法，无需模型学习，通过精确估计不确定性生成安全动作，在独轮车和二维四旋翼仿真中降低了安全违规率并提升了样本与计算效率。

通过基于滤波的概率数值ODE求解器传播模型不确定性

Propagating Model Uncertainty through Filtering-based Probabilistic Numerical ODE Solvers

针对ODE滤波器无法自动传播动力系统参数不确定性的问题，提出结合ODE滤波器与数值积分的新方法，可同时量化参数与数值不确定性，实验验证其估计结果与参考解高度吻合，且能避免大步长下的过度自信问题。

Probabilistic Ensemble of Collaborative Filters

Collaborative filtering is an important technique for recommendation. Whereas it has been repeatedly shown to be effective in previous work, its performance remains unsatisfactory in many real-world applications, especially those where the items or users are highly diverse. In this paper, we explore an ensemble-based framework to enhance the capability of a recommender in handling diverse data. Specifically, we formulate a probabilistic model which integrates the items, the users, as well as the associations between them into a generative process. On top of this formulation, we further derive …