Teaser: How seemingly trivial experiment design choices to simplify the evaluation of human-ML systems can yield misleading results.

越来越多的研究进行了人类主题评估，以研究为用户提供机器学习模型的解释是否可以帮助他们制定实际现实世界中的用例。但是，运行的用户研究具有挑战性且昂贵，因此每个研究通常只评估有限的不同设置，例如，研究通常只评估一些任意选择的解释方法。为了应对这些挑战和援助用户研究设计，我们介绍了用用例的模拟评估（Simevals）。 SIMEVALS涉及培训算法剂，以输入信息内容（例如模型解释），这些信息内容将在人类学科研究中提交给每个参与者，以预测感兴趣的用例的答案。算法代理的测试集精度提供了衡量下游用例信息内容的预测性。我们对三种现实世界用例（正向模拟，模型调试和反事实推理）进行全面评估，以证明Simevals可以有效地确定哪种解释方法将为每个用例提供帮助。这些结果提供了证据表明，Simevals可用于有效筛选一组重要的用户研究设计决策，例如在进行潜在昂贵的用户研究之前，选择应向用户提供哪些解释。

机器学习（ML）从业人员越来越多地承担着与非技术专家的价值观和目标保持一致的模型。但是，关于从业人员如何将域专业知识转化为ML更新的考虑不足。在本文中，我们考虑如何系统地捕获从业者和专家之间的互动。我们设计了一种分类法，以将专家反馈类型与从业者更新相匹配。从业者可以从观察或域级别的专家那里收到反馈，并将此反馈转换为数据集，损耗函数或参数空间的更新。我们回顾了ML和人类计算机互动中的现有工作，以描述这种反馈更高的分类法，并强调了不足以纳入非技术专家的反馈意见。我们以一系列的开放问题结尾，这些问题自然而然地源于我们提议的分类法和随后的调查。

当经过自动化决策时，决策主题将战略性地修改其可观察特征，他们认为可以最大限度地提高收到理想的结果的机会。在许多情况下，潜在的预测模型是故意保密的，以避免游戏并保持竞争优势。这种不透明度迫使决策主题依赖于制定战略功能修改时依赖不完整的信息。我们将这样的设置捕获作为贝叶斯劝说的游戏，其中决策者发送信号，例如动作建议，以便决定受激励他们采取理想的行动。我们制定决策者找到最佳贝叶斯激励兼容（BIC）行动推荐策略作为优化问题的问题，并通过线性程序表征解决方案。通过这种特征，我们观察到，虽然可以显着地简化了找到最佳BIC推荐策略的问题，但是解决该线性程序的计算复杂性与（1）决策主题的动作空间的相对大小紧密相关（2）基础预测模型利用的特征数。最后，我们提供了最佳BIC推荐政策的性能的界限，并表明与标准基线相比，它可能导致任意更好的结果。

The notion of concept drift refers to the phenomenon that the distribution generating the observed data changes over time. If drift is present, machine learning models may become inaccurate and need adjustment. Many technologies for learning with drift rely on the interleaved test-train error (ITTE) as a quantity which approximates the model generalization error and triggers drift detection and model updates. In this work, we investigate in how far this procedure is mathematically justified. More precisely, we relate a change of the ITTE to the presence of real drift, i.e., a changed posterior, and to a change of the training result under the assumption of optimality. We support our theoretical findings by empirical evidence for several learning algorithms, models, and datasets.

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.