Graphical models have exhibited their performance in numerous tasks ranging
from biological analysis to recommender systems. However, graphical models with
hub nodes are computationally difficult to fit, particularly when the dimension
of the data is large. To efficiently estimate the hub graphical models, we
introduce a two-phase algorithm. The proposed algorithm first generates a good
initial point via a dual alternating direction method of multipliers (ADMM),
and then warm starts a semismooth Newton (SSN) based augmented Lagrangian
method (ALM) to compute a solution that is accurate enough for practical tasks.
The sparsity structure of the generalized Jacobian ensures that the algorithm
can obtain a nice solution very efficiently. Comprehensive experiments on both
synthetic data and real data show that it obviously outperforms the existing
state-of-the-art algorithms. In particular, in some high dimensional tasks, it
can save more than 70\% of the execution time, meanwhile still achieves a
high-quality estimation.

图形模型中的算法通过引入一个两阶段算法来高效估计中心节点图形模型，在高维任务中执行时间减少 70％以上，同时仍然实现高质量的估计。

通过高效算法学习结构稀疏的 Hub 图 Lasso 模型

Learning the hub graphical Lasso model with the structured sparsity via  an efficient algorithm

By approximating posterior distributions with weighted samples, particle
filters (PFs) provide an efficient mechanism for solving non-linear sequential
state estimation problems. While the effectiveness of particle filters has been
recognised in various applications, the performance of particle filters relies
on the knowledge of dynamic models and measurement models, and the construction
of effective proposal distributions. An emerging trend in designing particle
filters is the differentiable particle filters (DPFs). By constructing particle
filters' components through neural networks and optimising them by gradient
descent, differentiable particle filters are a promising computational tool to
perform inference for sequence data in complex high-dimensional tasks such as
vision-based robot localisation. In this paper, we provide a review of recent
advances in differentiable particle filters and their applications. We place
special emphasis on different design choices of key components of
differentiable particle filters, including dynamic models, measurement models,
proposal distributions, optimisation objectives, and differentiable resampling
techniques.

本文综述了近年来不同的可微粒子滤波器及其应用，重点介绍了其中的关键组件设计，包括动态模型、测量模型、提议分布、优化目标和可微重采样技术。

可微粒子滤波器概述：数据自适应序贝叶斯推断

An overview of differentiable particle filters for data-adaptive sequential Bayesian inference

We propose a simple, practical, and intuitive approach for domain adaptation
in reinforcement learning. Our approach stems from the idea that the agent's
experience in the source domain should look similar to its experience in the
target domain. Building off of a probabilistic view of RL, we formally show
that we can achieve this goal by compensating for the difference in dynamics by
modifying the reward function. This modified reward function is simple to
estimate by learning auxiliary classifiers that distinguish source-domain
transitions from target-domain transitions. Intuitively, the modified reward
function penalizes the agent for visiting states and taking actions in the
source domain which are not possible in the target domain. Said another way,
the agent is penalized for transitions that would indicate that the agent is
interacting with the source domain, rather than the target domain. Our approach
is applicable to domains with continuous states and actions and does not
require learning an explicit model of the dynamics. On discrete and continuous
control tasks, we illustrate the mechanics of our approach and demonstrate its
scalability to high-dimensional tasks.

我们提出了一种简单、实用和直观的强化学习领域自适应方法，通过修改奖励函数，使用辅助分类器来区分源域和目标域，对源域中不可能出现的状态进行惩罚，适用于连续状态和动作的域，可扩展至高维任务。