概率主成分分析（PPCA）是高斯潜在变量模型的框架下主成分分析（PCA）的概率重构。为了提高PPCA的稳健性，已经提出将潜在的高斯分布改变为多元$ T $-DRIBIRATIONS。基于$ T $的表示，作为高斯分布的规模混合，分层模型用于实施。然而，在现有文献中，实现的分层模型不会产生等同的解释。在本文中，我们在高级多元$ T $ -PPCA框架和用于实现的层次模型之间存在两组等效关系。在这样做时，我们通过指定正确的对应来阐明文献中的当前歪曲。此外，我们讨论了理论和仿真研究的不同多元$ T $鲁棒PPCA方法的性能，并提出了一种新颖的蒙特卡罗期望 - 最大化（MCEM）算法，实现了一种常规类型的这种模型。

Artificial intelligence methods including deep neural networks (DNN) can provide rapid molecular classification of tumors from routine histology with accuracy that matches or exceeds human pathologists. Discerning how neural networks make their predictions remains a significant challenge, but explainability tools help provide insights into what models have learned when corresponding histologic features are poorly defined. Here, we present a method for improving explainability of DNN models using synthetic histology generated by a conditional generative adversarial network (cGAN). We show that cGANs generate high-quality synthetic histology images that can be leveraged for explaining DNN models trained to classify molecularly-subtyped tumors, exposing histologic features associated with molecular state. Fine-tuning synthetic histology through class and layer blending illustrates nuanced morphologic differences between tumor subtypes. Finally, we demonstrate the use of synthetic histology for augmenting pathologist-in-training education, showing that these intuitive visualizations can reinforce and improve understanding of histologic manifestations of tumor biology.

贝叶斯变量选择方法是适合和推断稀疏高维线性回归模型的强大技术。但是，许多在计算密集型上或需要对模型参数进行限制性的先验分布。基于可能性的惩罚方法在计算方面更友好，但是推理需要资源密集型的改装技术。在本文中，我们提出了一种有效而强大的贝叶斯方法，用于稀疏高维线性回归。通过使用插件的经验贝叶斯估算超参数的估计值，需要对参数的最小化假设。有效的最大后验概率（MAP）估计是通过使用分区和扩展期望最大化（ECM）算法完成的。结果是应用于稀疏高维线性回归的经验贝叶斯ECM（探针）算法。我们提出了估计未来价值预测的可靠和预测间隔的方法。我们将预测的经验特性和我们的预测推断与可比方法进行了比较，并通过大量的模拟研究和对癌细胞系药物反应研究的分析进行了比较。提出的方法在R软件包探针中实现。

State-of-the-art causal discovery methods usually assume that the observational data is complete. However, the missing data problem is pervasive in many practical scenarios such as clinical trials, economics, and biology. One straightforward way to address the missing data problem is first to impute the data using off-the-shelf imputation methods and then apply existing causal discovery methods. However, such a two-step method may suffer from suboptimality, as the imputation algorithm may introduce bias for modeling the underlying data distribution. In this paper, we develop a general method, which we call MissDAG, to perform causal discovery from data with incomplete observations. Focusing mainly on the assumptions of ignorable missingness and the identifiable additive noise models (ANMs), MissDAG maximizes the expected likelihood of the visible part of observations under the expectation-maximization (EM) framework. In the E-step, in cases where computing the posterior distributions of parameters in closed-form is not feasible, Monte Carlo EM is leveraged to approximate the likelihood. In the M-step, MissDAG leverages the density transformation to model the noise distributions with simpler and specific formulations by virtue of the ANMs and uses a likelihood-based causal discovery algorithm with directed acyclic graph constraint. We demonstrate the flexibility of MissDAG for incorporating various causal discovery algorithms and its efficacy through extensive simulations and real data experiments.

因果发现旨在从观察数据中学习因果图。迄今为止，大多数因果发现方法需要将数据存储在中央服务器中。但是，数据所有者逐渐拒绝分享他们的个性化数据以避免隐私泄漏，使这项任务通过切断第一步来更加麻烦。出现拼图：$ \ texit {如何从分散数据的原因关系推断出来自分散数据的因果关系？} $本文，具有数据的添加性噪声模型假设，我们参加了开发基于渐变的学习框架命名为DAG共享的渐变学习框架联邦因果发现（DS-FCD），可以在不直接触摸本地数据的情况下学习因果图，并自然地处理数据异质性。 DS-FCD受益于每个本地模型的两级结构。第一级别学习因果图并与服务器通信以获取来自其他客户端的模型信息，而第二级别近似于因果机制，并且从其自身的数据逐步更新以适应数据异质性。此外，DS-FCD通过利用平等的非循环性约束，将整体学习任务制定为连续优化问题，这可以通过梯度下降方法自然地解决。对合成和现实世界数据集的广泛实验验证了所提出的方法的功效。

In this paper, we propose a novel technique, namely INVALIDATOR, to automatically assess the correctness of APR-generated patches via semantic and syntactic reasoning. INVALIDATOR reasons about program semantic via program invariants while it also captures program syntax via language semantic learned from large code corpus using the pre-trained language model. Given a buggy program and the developer-patched program, INVALIDATOR infers likely invariants on both programs. Then, INVALIDATOR determines that a APR-generated patch overfits if: (1) it violates correct specifications or (2) maintains errors behaviors of the original buggy program. In case our approach fails to determine an overfitting patch based on invariants, INVALIDATOR utilizes a trained model from labeled patches to assess patch correctness based on program syntax. The benefit of INVALIDATOR is three-fold. First, INVALIDATOR is able to leverage both semantic and syntactic reasoning to enhance its discriminant capability. Second, INVALIDATOR does not require new test cases to be generated but instead only relies on the current test suite and uses invariant inference to generalize the behaviors of a program. Third, INVALIDATOR is fully automated. We have conducted our experiments on a dataset of 885 patches generated on real-world programs in Defects4J. Experiment results show that INVALIDATOR correctly classified 79% overfitting patches, accounting for 23% more overfitting patches being detected by the best baseline. INVALIDATOR also substantially outperforms the best baselines by 14% and 19% in terms of Accuracy and F-Measure, respectively.

When robots learn reward functions using high capacity models that take raw state directly as input, they need to both learn a representation for what matters in the task -- the task ``features" -- as well as how to combine these features into a single objective. If they try to do both at once from input designed to teach the full reward function, it is easy to end up with a representation that contains spurious correlations in the data, which fails to generalize to new settings. Instead, our ultimate goal is to enable robots to identify and isolate the causal features that people actually care about and use when they represent states and behavior. Our idea is that we can tune into this representation by asking users what behaviors they consider similar: behaviors will be similar if the features that matter are similar, even if low-level behavior is different; conversely, behaviors will be different if even one of the features that matter differs. This, in turn, is what enables the robot to disambiguate between what needs to go into the representation versus what is spurious, as well as what aspects of behavior can be compressed together versus not. The notion of learning representations based on similarity has a nice parallel in contrastive learning, a self-supervised representation learning technique that maps visually similar data points to similar embeddings, where similarity is defined by a designer through data augmentation heuristics. By contrast, in order to learn the representations that people use, so we can learn their preferences and objectives, we use their definition of similarity. In simulation as well as in a user study, we show that learning through such similarity queries leads to representations that, while far from perfect, are indeed more generalizable than self-supervised and task-input alternatives.

The latent space of autoencoders has been improved for clustering image data by jointly learning a t-distributed embedding with a clustering algorithm inspired by the neighborhood embedding concept proposed for data visualization. However, multivariate tabular data pose different challenges in representation learning than image data, where traditional machine learning is often superior to deep tabular data learning. In this paper, we address the challenges of learning tabular data in contrast to image data and present a novel Gaussian Cluster Embedding in Autoencoder Latent Space (G-CEALS) algorithm by replacing t-distributions with multivariate Gaussian clusters. Unlike current methods, the proposed approach independently defines the Gaussian embedding and the target cluster distribution to accommodate any clustering algorithm in representation learning. A trained G-CEALS model extracts a quality embedding for unseen test data. Based on the embedding clustering accuracy, the average rank of the proposed G-CEALS method is 1.4 (0.7), which is superior to all eight baseline clustering and cluster embedding methods on seven tabular data sets. This paper shows one of the first algorithms to jointly learn embedding and clustering to improve multivariate tabular data representation in downstream clustering.

An unbiased scene graph generation (SGG) algorithm referred to as Skew Class-balanced Re-weighting (SCR) is proposed for considering the unbiased predicate prediction caused by the long-tailed distribution. The prior works focus mainly on alleviating the deteriorating performances of the minority predicate predictions, showing drastic dropping recall scores, i.e., losing the majority predicate performances. It has not yet correctly analyzed the trade-off between majority and minority predicate performances in the limited SGG datasets. In this paper, to alleviate the issue, the Skew Class-balanced Re-weighting (SCR) loss function is considered for the unbiased SGG models. Leveraged by the skewness of biased predicate predictions, the SCR estimates the target predicate weight coefficient and then re-weights more to the biased predicates for better trading-off between the majority predicates and the minority ones. Extensive experiments conducted on the standard Visual Genome dataset and Open Image V4 \& V6 show the performances and generality of the SCR with the traditional SGG models.

In this paper we discuss the theory used in the design of an open source lightmorphic signatures analysis toolkit (LSAT). In addition to providing a core functionality, the software package enables specific optimizations with its modular and customizable design. To promote its usage and inspire future contributions, LSAT is publicly available. By using a self-supervised neural network and augmented machine learning algorithms, LSAT provides an easy-to-use interface with ample documentation. The experiments demonstrate that LSAT improves the otherwise tedious and error-prone tasks of translating lightmorphic associated data into usable spectrograms, enhanced with parameter tuning and performance analysis. With the provided mathematical functions, LSAT validates the nonlinearity encountered in the data conversion process while ensuring suitability of the forecasting algorithms.