Fairness is an essential factor for machine learning systems deployed in high-stake applications. Among all fairness notions, individual fairness, following a consensus that `similar individuals should be treated similarly,' is a vital notion to guarantee fair treatment for individual cases. Previous methods typically characterize individual fairness as a prediction-invariant problem when perturbing sensitive attributes, and solve it by adopting the Distributionally Robust Optimization (DRO) paradigm. However, adversarial perturbations along a direction covering sensitive information do not consider the inherent feature correlations or innate data constraints, and thus mislead the model to optimize at off-manifold and unrealistic samples. In light of this, we propose a method to learn and generate antidote data that approximately follows the data distribution to remedy individual unfairness. These on-manifold antidote data can be used through a generic optimization procedure with original training data, resulting in a pure pre-processing approach to individual unfairness, or can also fit well with the in-processing DRO paradigm. Through extensive experiments, we demonstrate our antidote data resists individual unfairness at a minimal or zero cost to the model's predictive utility.

第三方评级机构颁发的公司信贷评级是对公司信誉的量化评估。信贷评级与公司违约债务义务的可能性高度相关。这些评级在投资决策中起关键作用，这是关键风险因素之一。它们也是监管框架的核心，例如在计算金融机构必要的资本中，巴塞尔二世。能够预测评级变化将极大地使投资者和监管机构受益。在本文中，我们考虑了公司信用评级移民早期预测问题，该问题预测发行人的信用等级将根据当时的最新财务报告信息在12个月后升级，不变或降级。我们研究了不同标准的机器学习算法的有效性，并得出结论这些模型表现不佳。作为我们贡献的一部分，我们提出了一个新的多任务设想基于变压器的自动编码器（META）模型，以解决这个具有挑战性的问题。 META包括位置编码，基于变压器的自动编码器和多任务预测，以学习迁移预测和评级预测的有效表示。这使得元可以更好地探索一年后预测的培训阶段的历史数据。实验结果表明，元表现优于所有基线模型。

随着算法治理的快速发展，公平性已成为机器学习模型的强制性属性，以抑制无意的歧视。在本文中，我们着重于实现公平性的预处理方面，并提出了一种数据重新拨打的方法，该方法仅在培训阶段调整样本的重量。与通常为每个（子）组分配均匀权重的大多数以前的重新校正方法不同，我们对每个训练样本在与公平相关的数量和预测效用方面的影响进行颗粒片，并根据在从影响下的影响下对单个权重进行计算。公平和效用。实验结果表明，以前的方法以不可忽略的实用性成本达到公平性，而为了取得重大优势，我们的方法可以从经验上释放权衡并获得无需成本的公平就可以平等机会。与多个现实世界表格数据集中的基线方法相比，我们通过香草分类器和标准培训过程证明了通过香草分类器和标准培训过程的公平性。可在https://github.com/brandeis-machine-learning/influence-fairness上获得代码。

图像生成在学术界和工业领域提出了巨大的关注，特别是对于有条件和目标导向的图像生成，例如犯罪肖像和时装设计。虽然目前的研究已经沿着这个方向实现了初步结果，但它们总是将课堂标签集中在阶级标签中作为空间内容从潜伏向量随机产生的条件。边缘细节通常模糊，因为空间信息难以保持。鉴于此，我们提出了一种新型的空间受限的生成对抗网络（SCAGAN），其从潜伏向量中分离出空间约束，并使这些约束可行作为额外的可控信号。为了增强空间可控性，发电机网络专门设计用于逐步采用语义分割，潜在的传染媒介和属性级标签作为输入。此外，构造分段网络以对发电机施加空间约束。在实验上，我们在Celeba和Deepfashion数据集中提供视觉和定量结果，并证明所提出的Scang在控制空间内容以及产生高质量图像方面非常有效。

In this paper, we propose a robust 3D detector, named Cross Modal Transformer (CMT), for end-to-end 3D multi-modal detection. Without explicit view transformation, CMT takes the image and point clouds tokens as inputs and directly outputs accurate 3D bounding boxes. The spatial alignment of multi-modal tokens is performed implicitly, by encoding the 3D points into multi-modal features. The core design of CMT is quite simple while its performance is impressive. CMT obtains 73.0% NDS on nuScenes benchmark. Moreover, CMT has a strong robustness even if the LiDAR is missing. Code will be released at https://github.com/junjie18/CMT.

Dataset distillation has emerged as a prominent technique to improve data efficiency when training machine learning models. It encapsulates the knowledge from a large dataset into a smaller synthetic dataset. A model trained on this smaller distilled dataset can attain comparable performance to a model trained on the original training dataset. However, the existing dataset distillation techniques mainly aim at achieving the best trade-off between resource usage efficiency and model utility. The security risks stemming from them have not been explored. This study performs the first backdoor attack against the models trained on the data distilled by dataset distillation models in the image domain. Concretely, we inject triggers into the synthetic data during the distillation procedure rather than during the model training stage, where all previous attacks are performed. We propose two types of backdoor attacks, namely NAIVEATTACK and DOORPING. NAIVEATTACK simply adds triggers to the raw data at the initial distillation phase, while DOORPING iteratively updates the triggers during the entire distillation procedure. We conduct extensive evaluations on multiple datasets, architectures, and dataset distillation techniques. Empirical evaluation shows that NAIVEATTACK achieves decent attack success rate (ASR) scores in some cases, while DOORPING reaches higher ASR scores (close to 1.0) in all cases. Furthermore, we conduct a comprehensive ablation study to analyze the factors that may affect the attack performance. Finally, we evaluate multiple defense mechanisms against our backdoor attacks and show that our attacks can practically circumvent these defense mechanisms.

Few Shot Instance Segmentation (FSIS) requires models to detect and segment novel classes with limited several support examples. In this work, we explore a simple yet unified solution for FSIS as well as its incremental variants, and introduce a new framework named Reference Twice (RefT) to fully explore the relationship between support/query features based on a Transformer-like framework. Our key insights are two folds: Firstly, with the aid of support masks, we can generate dynamic class centers more appropriately to re-weight query features. Secondly, we find that support object queries have already encoded key factors after base training. In this way, the query features can be enhanced twice from two aspects, i.e., feature-level and instance-level. In particular, we firstly design a mask-based dynamic weighting module to enhance support features and then propose to link object queries for better calibration via cross-attention. After the above steps, the novel classes can be improved significantly over our strong baseline. Additionally, our new framework can be easily extended to incremental FSIS with minor modification. When benchmarking results on the COCO dataset for FSIS, gFSIS, and iFSIS settings, our method achieves a competitive performance compared to existing approaches across different shots, e.g., we boost nAP by noticeable +8.2/+9.4 over the current state-of-the-art FSIS method for 10/30-shot. We further demonstrate the superiority of our approach on Few Shot Object Detection. Code and model will be available.

This paper focuses on designing efficient models with low parameters and FLOPs for dense predictions. Even though CNN-based lightweight methods have achieved stunning results after years of research, trading-off model accuracy and constrained resources still need further improvements. This work rethinks the essential unity of efficient Inverted Residual Block in MobileNetv2 and effective Transformer in ViT, inductively abstracting a general concept of Meta-Mobile Block, and we argue that the specific instantiation is very important to model performance though sharing the same framework. Motivated by this phenomenon, we deduce a simple yet efficient modern \textbf{I}nverted \textbf{R}esidual \textbf{M}obile \textbf{B}lock (iRMB) for mobile applications, which absorbs CNN-like efficiency to model short-distance dependency and Transformer-like dynamic modeling capability to learn long-distance interactions. Furthermore, we design a ResNet-like 4-phase \textbf{E}fficient \textbf{MO}del (EMO) based only on a series of iRMBs for dense applications. Massive experiments on ImageNet-1K, COCO2017, and ADE20K benchmarks demonstrate the superiority of our EMO over state-of-the-art methods, \eg, our EMO-1M/2M/5M achieve 71.5, 75.1, and 78.4 Top-1 that surpass \textbf{SoTA} CNN-/Transformer-based models, while trading-off the model accuracy and efficiency well.

Benefiting from the intrinsic supervision information exploitation capability, contrastive learning has achieved promising performance in the field of deep graph clustering recently. However, we observe that two drawbacks of the positive and negative sample construction mechanisms limit the performance of existing algorithms from further improvement. 1) The quality of positive samples heavily depends on the carefully designed data augmentations, while inappropriate data augmentations would easily lead to the semantic drift and indiscriminative positive samples. 2) The constructed negative samples are not reliable for ignoring important clustering information. To solve these problems, we propose a Cluster-guided Contrastive deep Graph Clustering network (CCGC) by mining the intrinsic supervision information in the high-confidence clustering results. Specifically, instead of conducting complex node or edge perturbation, we construct two views of the graph by designing special Siamese encoders whose weights are not shared between the sibling sub-networks. Then, guided by the high-confidence clustering information, we carefully select and construct the positive samples from the same high-confidence cluster in two views. Moreover, to construct semantic meaningful negative sample pairs, we regard the centers of different high-confidence clusters as negative samples, thus improving the discriminative capability and reliability of the constructed sample pairs. Lastly, we design an objective function to pull close the samples from the same cluster while pushing away those from other clusters by maximizing and minimizing the cross-view cosine similarity between positive and negative samples. Extensive experimental results on six datasets demonstrate the effectiveness of CCGC compared with the existing state-of-the-art algorithms.

As one of the prevalent methods to achieve automation systems, Imitation Learning (IL) presents a promising performance in a wide range of domains. However, despite the considerable improvement in policy performance, the corresponding research on the explainability of IL models is still limited. Inspired by the recent approaches in explainable artificial intelligence methods, we proposed a model-agnostic explaining framework for IL models called R2RISE. R2RISE aims to explain the overall policy performance with respect to the frames in demonstrations. It iteratively retrains the black-box IL model from the randomized masked demonstrations and uses the conventional evaluation outcome environment returns as the coefficient to build an importance map. We also conducted experiments to investigate three major questions concerning frames' importance equality, the effectiveness of the importance map, and connections between importance maps from different IL models. The result shows that R2RISE successfully distinguishes important frames from the demonstrations.