Graph Neural Networks (GNNs) have shown satisfying performance on various graph learning tasks. To achieve better fitting capability, most GNNs are with a large number of parameters, which makes these GNNs computationally expensive. Therefore, it is difficult to deploy them onto edge devices with scarce computational resources, e.g., mobile phones and wearable smart devices. Knowledge Distillation (KD) is a common solution to compress GNNs, where a light-weighted model (i.e., the student model) is encouraged to mimic the behavior of a computationally expensive GNN (i.e., the teacher GNN model). Nevertheless, most existing GNN-based KD methods lack fairness consideration. As a consequence, the student model usually inherits and even exaggerates the bias from the teacher GNN. To handle such a problem, we take initial steps towards fair knowledge distillation for GNNs. Specifically, we first formulate a novel problem of fair knowledge distillation for GNN-based teacher-student frameworks. Then we propose a principled framework named RELIANT to mitigate the bias exhibited by the student model. Notably, the design of RELIANT is decoupled from any specific teacher and student model structures, and thus can be easily adapted to various GNN-based KD frameworks. We perform extensive experiments on multiple real-world datasets, which corroborates that RELIANT achieves less biased GNN knowledge distillation while maintaining high prediction utility.

With the development of technology and sharing economy, Airbnb as a famous short-term rental platform, has become the first choice for many young people to select. The issue of Airbnb's pricing has always been a problem worth studying. While the previous studies achieve promising results, there are exists deficiencies to solve. Such as, (1) the feature attributes of rental are not rich enough; (2) the research on rental text information is not deep enough; (3) there are few studies on predicting the rental price combined with the point of interest(POI) around the house. To address the above challenges, we proposes a multi-source information embedding(MSIE) model to predict the rental price of Airbnb. Specifically, we first selects the statistical feature to embed the original rental data. Secondly, we generates the word feature vector and emotional score combination of three different text information to form the text feature embedding. Thirdly, we uses the points of interest(POI) around the rental house information generates a variety of spatial network graphs, and learns the embedding of the network to obtain the spatial feature embedding. Finally, this paper combines the three modules into multi source rental representations, and uses the constructed fully connected neural network to predict the price. The analysis of the experimental results shows the effectiveness of our proposed model.

Deep latent variable models have achieved significant empirical successes in model-based reinforcement learning (RL) due to their expressiveness in modeling complex transition dynamics. On the other hand, it remains unclear theoretically and empirically how latent variable models may facilitate learning, planning, and exploration to improve the sample efficiency of RL. In this paper, we provide a representation view of the latent variable models for state-action value functions, which allows both tractable variational learning algorithm and effective implementation of the optimism/pessimism principle in the face of uncertainty for exploration. In particular, we propose a computationally efficient planning algorithm with UCB exploration by incorporating kernel embeddings of latent variable models. Theoretically, we establish the sample complexity of the proposed approach in the online and offline settings. Empirically, we demonstrate superior performance over current state-of-the-art algorithms across various benchmarks.

Achieving accurate and automated tumor segmentation plays an important role in both clinical practice and radiomics research. Segmentation in medicine is now often performed manually by experts, which is a laborious, expensive and error-prone task. Manual annotation relies heavily on the experience and knowledge of these experts. In addition, there is much intra- and interobserver variation. Therefore, it is of great significance to develop a method that can automatically segment tumor target regions. In this paper, we propose a deep learning segmentation method based on multimodal positron emission tomography-computed tomography (PET-CT), which combines the high sensitivity of PET and the precise anatomical information of CT. We design an improved spatial attention network(ISA-Net) to increase the accuracy of PET or CT in detecting tumors, which uses multi-scale convolution operation to extract feature information and can highlight the tumor region location information and suppress the non-tumor region location information. In addition, our network uses dual-channel inputs in the coding stage and fuses them in the decoding stage, which can take advantage of the differences and complementarities between PET and CT. We validated the proposed ISA-Net method on two clinical datasets, a soft tissue sarcoma(STS) and a head and neck tumor(HECKTOR) dataset, and compared with other attention methods for tumor segmentation. The DSC score of 0.8378 on STS dataset and 0.8076 on HECKTOR dataset show that ISA-Net method achieves better segmentation performance and has better generalization. Conclusions: The method proposed in this paper is based on multi-modal medical image tumor segmentation, which can effectively utilize the difference and complementarity of different modes. The method can also be applied to other multi-modal data or single-modal data by proper adjustment.

电子商务搜索的关键是如何最好地利用大型但嘈杂的日志数据。在本文中，我们在Instacart介绍了基于嵌入的杂货搜索模型。该系统通过基于两个塔式变压器的编码器体系结构学习查询和产品表示。为了解决冷门问题，我们专注于基于内容的功能。为了在嘈杂的数据上有效地培训模型，我们提出了一种自我分歧学习方法和级联培训方法。Accon是一个离线人类评估数据集，我们在召回@20方面取得了10％的相对改善，对于在线A/B测试，我们每次搜索（CAPS）获得4.1％的Cart-Addds（CAPS）和1.5％的总商品价值（GMV）改进。我们描述了如何训练和部署基于嵌入的搜索模型，并对我们方法的有效性进行详细分析。

激活压缩训练〜（ACT）已被证明是减少训练深神经网络中记忆消耗的一种有希望的方法。但是，现有的ACT工作依赖于在深神经网络（DNN）训练期间寻找最佳的位宽度以减少量化噪声，从而使过程变得复杂且透明。为此，我们提出了一种简单有效的DNN培训方法。我们的方法是由观察结果激励的：\ emph {DNN向后传播主要取决于激活图的低频组分〜（LFC），而不是高频组件〜（HFC）}。它表明激活图的HFC在DNN训练过程中是高度冗余和可压缩的，这激发了我们提出的双重激活精度〜（分裂）。在培训期间，分裂估计激活图的LFC和HFC，并将HFC压缩到低精度副本中以消除冗余。这可以大大减少记忆消耗，而不会对DNN向后传播的精度产生负面影响。这样，部门可以实现可比的表现与正常培训。三个基准数据集的实验结果表明，在记忆消耗，模型准确性和跑步速度方面，分裂的表现优于最先进的基线方法。

关于公平建模的现有工作通常假设所有实例的敏感属性都已完全可用，由于获取敏感信息的高成本，在许多现实世界中，这可能并非如此。当未披露或可用的敏感属性时，需要手动注释培训数据的一小部分以减轻偏见。但是，跨不同敏感组的偏斜分布保留了带注释的子集中原始数据集的偏度，这导致了非最佳偏置缓解。为了应对这一挑战，我们提出了对歧视（APOD）的积极惩罚，这是一个交互式框架，以指导有限的注释以最大程度地消除算法偏见的影响。拟议的APOD将歧视惩罚与主动实例选择集成在一起，以有效利用有限的注释预算，从理论上讲，它可以限制算法偏见。根据五个基准数据集的评估，APOD在有限的注释预算下优于最先进的基线方法，并显示出与完全注释的偏见缓解相当的性能，这表明APOD可以使真实世界应用程序受益于敏感信息时的应用是有限的。

尽管取得了令人鼓舞的结果，但最先进的交互式强化学习方案依赖于以连续监控或预定义的规则的形式从顾问专家那里获得监督信号，这不可避免地导致了繁琐而昂贵的学习过程。在本文中，我们介绍了一项新型的倡议顾问，在循环演员批判框架中被称为Ask-AC，该框架用双向学习者的实用主义者代替了单方面的顾问指导机制，从而实现了自定义的和有效的范围学习者和顾问之间的消息交换。 Ask-AC的核心是两个互补的组件，即动作请求者和自适应状态选择器，可以很容易地将其纳入各种离散的参与者 - 批判性架构中。前一个组件允许代理商在不确定状态的存在下首次寻求顾问干预，而后者则确定了前者可能遗漏的不稳定状态，尤其是在环境变化时，然后学会了促进对此类国家的询问行动。对固定环境和非平稳环境以及不同参与者 - 评分骨架的实验结果表明，所提出的框架显着提高了代理的学习效率，并与连续顾问监控获得的框架与表现相同。

学习的推荐系统可能会无意间泄露有关其培训数据的信息，从而导致侵犯隐私行为。我们调查了推荐系统通过成员推理面临的隐私威胁。在这种攻击中，对手旨在推断用户的数据是否用于训练目标推荐人。为了实现这一目标，以前的工作使用了阴影推荐人来为攻击模型得出训练数据，然后通过计算用户历史互动和推荐项目之间的差异向量来预测成员资格。最先进的方法面临两个具有挑战性的问题：（1）由于阴影和目标推荐人之间的差距，攻击模型的培训数据偏见，并且（2）推荐人中的隐藏状态没有观察到，导致估计不准确差矢量。为了解决上述局限性，我们提出了针对推荐系统（DL-MIA）框架的成员推理攻击的偏见学习，该框架具有四个主要组件：（1）差异向量生成器，（2）分发式编码器，（3）重量估算器和（4）攻击模型。为了减轻推荐人之间的差距，设计了基于变异的自动编码器（VAE）的分解编码器，以识别推荐人不变和特定功能。为了减少估计偏差，我们设计了一个权重估计器，为每个差异向量分配了真实级别的得分，以指示估计精度。我们对三个现实世界数据集的一般推荐人和顺序推荐人评估了DL-MIA。实验结果表明，DL-MIA有效地减轻了同时减轻培训和估计的偏见，并实现了最先进的攻击性能。

本文研究了用于多机构增强学习的政策优化算法。我们首先在全信息设置中提出了针对两人零和零和马尔可夫游戏的算法框架，其中每次迭代均使用一个策略更新，使用某个矩阵游戏算法在每个状态下进行策略更新，并带有一个带有特定的值更新步骤学习率。该框架统一了许多现有和新的政策优化算法。我们表明，只要矩阵游戏算法在每种状态下，该算法的州平均策略会收敛到游戏的近似NASH平衡（NE），只要矩阵游戏算法在每个状态下都具有低称重的遗憾价值更新。接下来，我们证明，该框架与每个状态（和平滑值更新）的乐观跟踪定制领导者（oftrl）算法可以找到$ \ Mathcal {\ widetilde {o}}（t^{ - 5 /6}）$ t $迭代中的$近似NE，并且具有稍微修改的值更新规则的类似算法可实现更快的$ \ Mathcal {\ widetilde {o}}}}（t^{ - 1}）$收敛率。这些改进了当前最佳$ \ Mathcal {\ widetilde {o}}}（t^{ - 1/2}）$对称策略优化类型算法的速率。我们还将此算法扩展到多玩家通用-SUM Markov游戏，并显示$ \ MATHCAL {\ widetilde {o}}}（t^{ - 3/4}）$收敛率与粗相关均衡（CCE）。最后，我们提供了一个数值示例来验证我们的理论并研究平滑价值更新的重要性，并发现使用“渴望”的价值更新（等同于独立的自然策略梯度算法）也可能会大大减慢收敛性，即使在$ h = 2 $层的简单游戏。