几十年来，对信用违约风险的预测一直是一个重要的研究领域。传统上，由于其准确性和解释性，逻辑回归被广泛认为是解决方案。作为最近的趋势，研究人员倾向于使用更复杂和高级的机器学习方法来提高预测的准确性。尽管某些非线性机器学习方法具有更好的预测能力，但通常认为它们缺乏金融监管机构的解释性。因此，它们尚未被广泛应用于信用风险评估中。我们引入了一个具有选择性选项的神经网络，以通过区分数据集来通过线性模型来解释，以提高可解释性。我们发现，对于大多数数据集，逻辑回归将足够，准确性合理。同时，对于某些特定的数据部分，浅神经网络模型可以提高精确度，而无需显着牺牲可解释性。

多年来，机器学习方法一直在各种领域（包括计算机视觉和自然语言处理）中使用。尽管机器学习方法比传统方法显着改善了模型性能，但它们的黑盒结构使研究人员难以解释结果。对于高度监管的金融行业，透明度，解释性和公平性同样重要，甚至比准确性重要。没有满足受管制要求的情况，即使是高度准确的机器学习方法也不太可能被接受。我们通过引入一种新颖的透明和可解释的机器学习算法来解决这个问题，称为神经添加剂模型的通用手套。神经添加剂模型的广义手套将特征分为三类：线性特征，单个非线性特征和相互作用的非线性特征。此外，最后类别中的交互仅是本地的。线性和非线性组件通过逐步选择算法区分，并通过应用加法分离标准仔细验证相互作用的组。经验结果表明，神经添加剂模型的广义手套可通过最简单的体系结构提供最佳的精度，从而可以采用高度准确，透明且可解释的机器学习方法。

数十年来，对信用违约风险的预测一直是一个积极的研究领域。从历史上看，逻辑回归由于遵守法规要求而被用作主要工具：透明度，解释性和公平性。近年来，研究人员越来越多地使用复杂和先进的机器学习方法来提高预测准确性。即使机器学习方法可以潜在地提高模型的准确性，但它会使简单的逻辑回归复杂化，会使解释性恶化并经常违反公平性。在没有法规要求的情况下，公司即使是高度准确的机器学习方法也不太可能被公司接受信用评分。在本文中，我们介绍了一类新颖的单调神经添加剂模型，这些模型通过简化神经网络体系结构并实施单调性来满足调节要求。通过利用神经添加剂模型的特殊体系结构特征，单调神经添加剂模型有效地违反了单调性。因此，训练的计算成本单调神经添加剂模型类似于训练神经添加剂模型的计算成本，作为免费午餐。我们通过经验结果证明，我们的新模型与Black-Box完全连接的神经网络一样准确，提供了一种高度准确且受调节的机器学习方法。

神经网络的使用在各种应用中都非常成功。但是，最近已经观察到，在分布转移的条件下，很难概括神经网络的性能。已经做出了几项努力，以确定潜在的分数输入。尽管现有文献在图像和文本数据方面取得了重大进展，但财务已被忽略。本文的目的是调查信用评分问题的分配变化，这是金融最重要的应用之一。对于潜在的分布转移问题，我们提出了一个新颖的两阶段模型。使用分布外检测方法，首先将数据分为自信和不自信的集合。作为第二步，我们利用均值优化的域知识来为不自信的样本提供可靠的界限。使用经验结果，我们证明了我们的模型为绝大多数数据集提供了可靠的预测。只是数据集的一小部分很难判断，我们将其留在了人类的判断中。基于两阶段模型，已经做出了高度自信的预测，并且与该模型相关的潜在风险已大大降低。

Deep learning models can achieve high accuracy when trained on large amounts of labeled data. However, real-world scenarios often involve several challenges: Training data may become available in installments, may originate from multiple different domains, and may not contain labels for training. Certain settings, for instance medical applications, often involve further restrictions that prohibit retention of previously seen data due to privacy regulations. In this work, to address such challenges, we study unsupervised segmentation in continual learning scenarios that involve domain shift. To that end, we introduce GarDA (Generative Appearance Replay for continual Domain Adaptation), a generative-replay based approach that can adapt a segmentation model sequentially to new domains with unlabeled data. In contrast to single-step unsupervised domain adaptation (UDA), continual adaptation to a sequence of domains enables leveraging and consolidation of information from multiple domains. Unlike previous approaches in incremental UDA, our method does not require access to previously seen data, making it applicable in many practical scenarios. We evaluate GarDA on two datasets with different organs and modalities, where it substantially outperforms existing techniques.

The development of social media user stance detection and bot detection methods rely heavily on large-scale and high-quality benchmarks. However, in addition to low annotation quality, existing benchmarks generally have incomplete user relationships, suppressing graph-based account detection research. To address these issues, we propose a Multi-Relational Graph-Based Twitter Account Detection Benchmark (MGTAB), the first standardized graph-based benchmark for account detection. To our knowledge, MGTAB was built based on the largest original data in the field, with over 1.55 million users and 130 million tweets. MGTAB contains 10,199 expert-annotated users and 7 types of relationships, ensuring high-quality annotation and diversified relations. In MGTAB, we extracted the 20 user property features with the greatest information gain and user tweet features as the user features. In addition, we performed a thorough evaluation of MGTAB and other public datasets. Our experiments found that graph-based approaches are generally more effective than feature-based approaches and perform better when introducing multiple relations. By analyzing experiment results, we identify effective approaches for account detection and provide potential future research directions in this field. Our benchmark and standardized evaluation procedures are freely available at: https://github.com/GraphDetec/MGTAB.

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.

Compressed videos often exhibit visually annoying artifacts, known as Perceivable Encoding Artifacts (PEAs), which dramatically degrade video visual quality. Subjective and objective measures capable of identifying and quantifying various types of PEAs are critical in improving visual quality. In this paper, we investigate the influence of four spatial PEAs (i.e. blurring, blocking, bleeding, and ringing) and two temporal PEAs (i.e. flickering and floating) on video quality. For spatial artifacts, we propose a visual saliency model with a low computational cost and higher consistency with human visual perception. In terms of temporal artifacts, self-attention based TimeSFormer is improved to detect temporal artifacts. Based on the six types of PEAs, a quality metric called Saliency-Aware Spatio-Temporal Artifacts Measurement (SSTAM) is proposed. Experimental results demonstrate that the proposed method outperforms state-of-the-art metrics. We believe that SSTAM will be beneficial for optimizing video coding techniques.

We propose a distributionally robust return-risk model for Markov decision processes (MDPs) under risk and reward ambiguity. The proposed model optimizes the weighted average of mean and percentile performances, and it covers the distributionally robust MDPs and the distributionally robust chance-constrained MDPs (both under reward ambiguity) as special cases. By considering that the unknown reward distribution lies in a Wasserstein ambiguity set, we derive the tractable reformulation for our model. In particular, we show that that the return-risk model can also account for risk from uncertain transition kernel when one only seeks deterministic policies, and that a distributionally robust MDP under the percentile criterion can be reformulated as its nominal counterpart at an adjusted risk level. A scalable first-order algorithm is designed to solve large-scale problems, and we demonstrate the advantages of our proposed model and algorithm through numerical experiments.

Witnessing the impressive achievements of pre-training techniques on large-scale data in the field of computer vision and natural language processing, we wonder whether this idea could be adapted in a grab-and-go spirit, and mitigate the sample inefficiency problem for visuomotor driving. Given the highly dynamic and variant nature of the input, the visuomotor driving task inherently lacks view and translation invariance, and the visual input contains massive irrelevant information for decision making, resulting in predominant pre-training approaches from general vision less suitable for the autonomous driving task. To this end, we propose PPGeo (Policy Pre-training via Geometric modeling), an intuitive and straightforward fully self-supervised framework curated for the policy pretraining in visuomotor driving. We aim at learning policy representations as a powerful abstraction by modeling 3D geometric scenes on large-scale unlabeled and uncalibrated YouTube driving videos. The proposed PPGeo is performed in two stages to support effective self-supervised training. In the first stage, the geometric modeling framework generates pose and depth predictions simultaneously, with two consecutive frames as input. In the second stage, the visual encoder learns driving policy representation by predicting the future ego-motion and optimizing with the photometric error based on current visual observation only. As such, the pre-trained visual encoder is equipped with rich driving policy related representations and thereby competent for multiple visuomotor driving tasks. Extensive experiments covering a wide span of challenging scenarios have demonstrated the superiority of our proposed approach, where improvements range from 2% to even over 100% with very limited data. Code and models will be available at https://github.com/OpenDriveLab/PPGeo.