Graph unlearning has emerged as an essential tool for safeguarding user
privacy and mitigating the negative impacts of undesirable data. Meanwhile, the
advent of dynamic graph neural networks (DGNNs) marks a significant advancement
due to their superior capability in learning from dynamic graphs, which
encapsulate spatial-temporal variations in diverse real-world applications
(e.g., traffic forecasting). With the increasing prevalence of DGNNs, it
becomes imperative to investigate the implementation of dynamic graph
unlearning. However, current graph unlearning methodologies are designed for
GNNs operating on static graphs and exhibit limitations including their serving
in a pre-processing manner and impractical resource demands. Furthermore, the
adaptation of these methods to DGNNs presents non-trivial challenges, owing to
the distinctive nature of dynamic graphs. To this end, we propose an effective,
efficient, model-agnostic, and post-processing method to implement DGNN
unlearning. Specifically, we first define the unlearning requests and formulate
dynamic graph unlearning in the context of continuous-time dynamic graphs.
After conducting a role analysis on the unlearning data, the remaining data,
and the target DGNN model, we propose a method called Gradient Transformation
and a loss function to map the unlearning request to the desired parameter
update. Evaluations on six real-world datasets and state-of-the-art DGNN
backbones demonstrate its effectiveness (e.g., limited performance drop even
obvious improvement) and efficiency (e.g., at most 7.23$\times$ speed-up)
outperformance, and potential advantages in handling future unlearning requests
(e.g., at most 32.59$\times$ speed-up).

我们提出了一种有效、高效、模型无关的后处理方法来实现动态图神经网络的去学习，通过定义去学习请求并在连续时间动态图的背景下形成动态图的去学习，通过对去学习数据、剩余数据和目标动态图神经网络模型进行角色分析，我们提出了一种称为梯度变换和损失函数的方法，将去学习请求映射到所需的参数更新。我们在六个真实世界数据集和最先进的动态图神经网络骨干上进行评估，证明了其效果（例如，性能下降有限，甚至明显改进），效率（例如，最多 7.23 倍加速）和处理未来去学习请求的潜在优势（例如，最多 32.59 倍加速）。

梯度转化：面向动态图神经网络的高效和模型无关的遗忘

Gradient Transformation: Towards Efficient and Model-Agnostic Unlearning  for Dynamic Graph Neural Networks

Predicting 3D room layout from single image is a challenging task with many
applications. In this paper, we propose a new training and post-processing
method for 3D room layout estimation, built on a recent state-of-the-art 3D
room layout estimation model. Experimental results show our method outperforms
state-of-the-art approaches by a large margin in predicting visible room
layout. Our method has obtained the 3rd place in 2020 Holistic Scene Structures
for 3D Vision Workshop.

本文提出了基于最新的三维室内布局估计模型的训练和后处理方法，实验结果显示我们的方法在预测可见室内布局方面比现有方法大幅优越，并在 2020 年全面场景结构与 3D 视觉研讨会获得了第三名。

超越曼哈顿世界假设的三维房间布局估计

3D Room Layout Estimation Beyond the Manhattan World Assumption

Tree ensembles, such as random forest and boosted trees, are renowned for
their high prediction performance, whereas their interpretability is critically
limited. In this paper, we propose a post processing method that improves the
model interpretability of tree ensembles. After learning a complex tree
ensembles in a standard way, we approximate it by a simpler model that is
interpretable for human. To obtain the simpler model, we derive the EM
algorithm minimizing the KL divergence from the complex ensemble. A synthetic
experiment showed that a complicated tree ensemble was approximated reasonably
as interpretable.

该研究提出一种后处理方法通过使用 EM 算法和简化模型来提高树模型的可解释性，并进行了人工合成实验以证明该方法的可行性。