基于鲁棒条件扩散模型的结构监测数据概率质量评估

通过渐进分位数获得抗离群值能力：快速算法与理论研究

Gaining Outlier Resistance with Progressive Quantiles: Fast Algorithms and Theoretical Studies

本文提出基于渐进分位数和显式离群程度参数的抗离群值估计框架，并开发快速可扩展算法，在低维和高维下均达到极小极大最优，实验验证了其在回归、分类和神经网络中的稳健性能。

Partial Wasserstein and Maximum Mean Discrepancy distances for bridging the gap between outlier detection and drift detection

With the rise of machine learning and deep learning based applications in practice, monitoring, i.e. verifying that these operate within specification, has become an important practical problem. An important aspect of this monitoring is to check whether the inputs (or intermediates) have strayed from the distribution they were validated for, which can void the performance assurances obtained during testing. There are two common approaches for this. The, perhaps, more classical one is outlier detection or novelty detection, where, for a single input we ask whether it is an outlier, i.e. exceedi…

Lumbermark：通过切断互达最小生成树实现鲁棒分裂聚类

Lumbermark: Resistant Clustering by Chopping Up Mutual Reachability Minimum Spanning Trees

Lumbermark是一种鲁棒的分裂聚类算法，通过切断互达最小生成树中的大枝来检测不同大小、密度和形状的簇。该算法通过互达距离平滑数据分布，减少低密度对象的影响，并提供用户指定大小的聚类划分。

Detecting and handling outlying trajectories in irregularly sampled functional datasets

Outlying curves often occur in functional or longitudinal datasets, and can be very influential on parameter estimators and very hard to detect visually. In this article we introduce estimators of the mean and the principal components that are resistant to, and then can be used for detection of, outlying sample trajectories. The estimators are based on reduced-rank t-models and are specifically aimed at sparse and irregularly sampled functional data. The outlier-resistance properties of the estimators and their relative efficiency for noncontaminated data are studied theoretically and by simul…

鲁棒滤波——新型统计学习与推理算法及其应用

Robust Filtering -- Novel Statistical Learning and Inference Algorithms with Applications

针对现实场景中噪声统计特性未知的各类测量异常限制传统滤波方法性能的问题，本研究提出新型鲁棒非线性滤波及扩展方法，结合多种近似技术并推导理论估计极限，经多场景验证有效，可支撑多领域智能决策及相关AI方向拓展。

稀疏鲁棒主成分分析用于多源数据

Sparse outlier-robust PCA for multi-source data

本文提出一种稀疏鲁棒PCA方法，用于多源数据联合分析，同时选择重要特征、检测稀疏模式并鲁棒于异常值。

Monitoring and explainability of models in production

The machine learning lifecycle extends beyond the deployment stage. Monitoring deployed models is crucial for continued provision of high quality machine learning enabled services. Key areas include model performance and data monitoring, detecting outliers and data drift using statistical techniques, and providing explanations of historic predictions. We discuss the challenges to successful implementation of solutions in each of these areas with some recent examples of production ready solutions using open source tools.

空间依赖性的非参数监测

Nonparametric Monitoring of Spatial Dependence

本文提出基于空间序数模式（SOPs）的非参数控制图，无需Phase-I分析即可监测空间依赖性，并结合Box-Pierce统计量揭示高阶依赖，在非线性、双边依赖及含离群值场景下优于传统参数方法，并通过德国降雨、乌克兰火灾和纺织品缺陷等真实数据验证其广泛实用性。

基于深度神经网络的单调单指标模态回归用于非高斯牙周数据

A Monotone Single-Index Modal Regression Powered by Deep Neural Networks for Non-Gaussian Periodontal Data

本文提出一种基于深度神经网络的单调单指标模态回归框架，结合两段尺度Student-t分布与单调性约束神经网络，用于分析偏态重尾的牙周探诊深度数据，实现稳健估计并保持临床可解释性。

Efficient Low-Rank Semidefinite Programming with Robust Loss Functions

In real-world applications, it is important for machine learning algorithms to be robust against data outliers or corruptions. In this paper, we focus on improving the robustness of a large class of learning algorithms that are formulated as low-rank semi-definite programming (SDP) problems. Traditional formulations use square loss, which is notorious for being sensitive to outliers. We propose to replace this with more robust noise models, including the $\ell_1$-loss and other nonconvex losses. However, the resultant optimization problem becomes difficult as the objective is no longer convex …