在缺乏真实标签的情况下使用马尔可夫条件与最小边数选择DAG模型

Causal Structure Discovery between Clusters of Nodes Induced by Latent Factors

We consider the problem of learning the structure of a causal directed acyclic graph (DAG) model in the presence of latent variables. We define latent factor causal models (LFCMs) as a restriction on causal DAG models with latent variables, which are composed of clusters of observed variables that share the same latent parent and connections between these clusters given by edges pointing from the observed variables to latent variables. LFCMs are motivated by gene regulatory networks, where regulatory edges, corresponding to transcription factors, connect spatially clustered genes. We show iden…

通过协方差矩阵的Cholesky分解恢复含潜变量的线性因果模型

Recovering Linear Causal Models with Latent Variables via Cholesky Factorization of Covariance Matrix

本文提出基于协方差矩阵Cholesky分解的快速DAG恢复算法，并通过结合优化过程处理含潜变量的因果结构恢复，在合成与真实数据上取得最先进性能。

LOCAL：基于方向矩阵学习的时间序列数据因果结构推断方法

LOCAL: Learning with Orientation Matrix to Infer Causal Structure from Time Series Data

针对时间序列因果DAG发现现有方法可扩展性差的问题，提出基于准极大似然评分的LOCAL方法，结合ACML和DGPL模块提升效率与可解释性，实验证明其性能显著优于现有方法。

Interventional Experiment Design for Causal Structure Learning

It is known that from purely observational data, a causal DAG is identifiable only up to its Markov equivalence class, and for many ground truth DAGs, the direction of a large portion of the edges will be remained unidentified. The golden standard for learning the causal DAG beyond Markov equivalence is to perform a sequence of interventions in the system and use the data gathered from the interventional distributions. We consider a setup in which given a budget $k$, we design $k$ interventions non-adaptively. We cast the problem of finding the best intervention target set as an optimization p…

GENESIS：迈向可解释的因果发现

GENESIS: Towards Explainable Causal Discovery

该研究提出可解释混合因果发现框架GENESIS，实现100%决策可追溯性，在多数基准数据集上性能优于纯统计方法，可与先进LLM辅助方法媲美。

TSI-GAN: Unsupervised Time Series Anomaly Detection using Convolutional Cycle-Consistent Generative Adversarial Networks

Anomaly detection is widely used in network intrusion detection, autonomous driving, medical diagnosis, credit card frauds, etc. However, several key challenges remain open, such as lack of ground truth labels, presence of complex temporal patterns, and generalizing over different datasets. This paper proposes TSI-GAN, an unsupervised anomaly detection model for time-series that can learn complex temporal patterns automatically and generalize well, i.e., no need for choosing dataset-specific parameters, making statistical assumptions about underlying data, or changing model architectures. To a…

零人工标注的增益标注

Salutary Labeling with Zero Human Annotation

本文提出增益标注方法，利用影响函数自动为信息量最大的样本分配最具正向影响的标签，无需人工标注，在九个基准数据集上超越传统主动学习策略，并探索了LLM微调应用。

A Contextual Bandit Approach for Stream-Based Active Learning

Contextual bandit algorithms -- a class of multi-armed bandit algorithms that exploit the contextual information -- have been shown to be effective in solving sequential decision making problems under uncertainty. A common assumption adopted in the literature is that the realized (ground truth) reward by taking the selected action is observed by the learner at no cost, which, however, is not realistic in many practical scenarios. When observing the ground truth reward is costly, a key challenge for the learner is how to judiciously acquire the ground truth by assessing the benefits and costs i…

迈向医学图像中任意分割的无真实标注评估

Towards Ground-truth-free Evaluation of Any Segmentation in Medical Images

本文提出无真实标注评估模型 EvanySeg，通过分析输入图像与分割预测的连贯性及一致性，利用回归监督学习评估 SAM 及其变体在医学图像中的分割质量，可应用于识别低质量样本、无标注基准测试及人机协作预警等任务。

Domain Adaptation via Teacher-Student Learning for End-to-End Speech Recognition

Teacher-student (T/S) has shown to be effective for domain adaptation of deep neural network acoustic models in hybrid speech recognition systems. In this work, we extend the T/S learning to large-scale unsupervised domain adaptation of an attention-based end-to-end (E2E) model through two levels of knowledge transfer: teacher's token posteriors as soft labels and one-best predictions as decoder guidance. To further improve T/S learning with the help of ground-truth labels, we propose adaptive T/S (AT/S) learning. Instead of conditionally choosing from either the teacher's soft token posterior…