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Ping Ma

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8 papers

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AAAI Conference 2026 Conference Paper

DCMM-Transformer: Degree-Corrected Mixed-Membership Attention for Medical Imaging

Huimin Cheng
Xiaowei Yu
Shushan Wu
Luyang Fang
Chao Cao
Jing Zhang
Tianming Liu
Dajiang Zhu

Medical images exhibit latent anatomical groupings, such as organs, tissues, and pathological regions, that standard Vision Transformers (ViTs) fail to exploit. While recent work like SBM-Transformer attempts to incorporate such structures through stochastic binary masking, they suffer from non-differentiability, training instability, and the inability to model complex community structure. We present DCMM-Transformer, a novel ViT architecture for medical image analysis that incorporates a Degree-Corrected Mixed-Membership (DCMM) model as an additive bias in self-attention. Unlike prior approaches that rely on multiplicative masking and binary sampling, our method introduces community structure and degree heterogeneity in a fully differentiable and interpretable manner. Comprehensive experiments across diverse medical imaging datasets, including brain, chest, breast, and ocular modalities, demonstrate the superior performance and generalizability of the proposed approach. Furthermore, the learned group structure and structured attention modulation substantially enhance interpretability by yielding attention maps that are anatomically meaningful and semantically coherent.

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EAAI Journal 2025 Journal Article

Rolling bearings remaining useful life estimation using digital twin and physics-informed methods with uncertainty quantification

Fengjin Gong
Ping Ma
Hongli Zhang
Cong Wang
Xinkai Li
Yinfei Wu

This paper presents an innovative framework for monitoring the degradation process of rolling bearings, predicting their remaining service life and quantifying the associated uncertainties. By integrating digital twin technology with artificial intelligence, the framework significantly enhances the accuracy and reliability of remaining service life predictions for rolling bearings. First, an advanced dynamic model of the rolling bearing is developed, forming the foundation for the digital twin to accurately simulate the bearing's operating conditions. Next, a dynamic update method for the digital twin model is proposed, based on a backpropagation neural network, which ensuring real-time adaptation to changes in actual bearing conditions. Building on this, an innovative uncertainty-quantifying physics-informed neural network is introduced, which leverages a dropout technique. This network takes the output of the digital twin model as input to predict the remaining service life of the bearing while quantifying the uncertainty. The performance of the proposed framework is evaluated through experiments, demonstrating its ability to accurately reflect the bearing's operational state and predict its remaining service life with quantified uncertainty. The experimental results show that the mean absolute error and root mean square error of this framework are 0. 071 and 0. 084, respectively, which are both significantly better than the 0. 100 and 0. 118 observed for other methods, fully validating the superiority of this method.

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JMLR Journal 2022 Journal Article

Asymptotic Analysis of Sampling Estimators for Randomized Numerical Linear Algebra Algorithms

Ping Ma
Yongkai Chen
Xinlian Zhang
Xin Xing
Jingyi Ma
Michael W. Mahoney

The statistical analysis of Randomized Numerical Linear Algebra (RandNLA) algorithms within the past few years has mostly focused on their performance as point estimators. However, this is insufficient for conducting statistical inference, e.g., constructing confidence intervals and hypothesis testing, since the distribution of the estimator is lacking. In this article, we develop an asymptotic analysis to derive the distribution of RandNLA sampling estimators for the least-squares problem. In particular, we derive the asymptotic distribution of a general sampling estimator with arbitrary sampling probabilities in a fixed design setting. The analysis is conducted in two complementary settings, i.e., when the objective of interest is to approximate the full sample estimator, and when it is to infer the underlying ground truth model parameters. For each setting, we show that the sampling estimator is asymptotically normally distributed under mild regularity conditions. Moreover, the sampling estimator is asymptotically unbiased in both settings. Based on our asymptotic analysis, we use two criteria, the Asymptotic Mean Squared Error (AMSE) and the Expected Asymptotic Mean Squared Error (EAMSE), to identify optimal sampling probabilities. Several of these optimal sampling probability distributions are new to the literature, e.g., the root leverage sampling estimator and the predictor length sampling estimator. Our theoretical results clarify the role of leverage in the sampling process, and our empirical results demonstrate improvements over existing methods. [abs] [ pdf ][ bib ] &copy JMLR 2022. ( edit, beta )

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JBHI Journal 2020 Journal Article

A Novel Intelligent Computational Approach to Model Epidemiological Trends and Assess the Impact of Non-Pharmacological Interventions for COVID-19

Jinchang Ren
Yijun Yan
Huimin Zhao
Ping Ma
Jaime Zabalza
Zain Hussain
Shaoming Luo
Qingyun Dai

The novel coronavirus disease 2019 (COVID-19) pandemic has led to a worldwide crisis in public health. It is crucial we understand the epidemiological trends and impact of non-pharmacological interventions (NPIs), such as lockdowns for effective management of the disease and control of its spread. We develop and validate a novel intelligent computational model to predict epidemiological trends of COVID-19, with the model parameters enabling an evaluation of the impact of NPIs. By representing the number of daily confirmed cases (NDCC) as a time-series, we assume that, with or without NPIs, the pattern of the pandemic satisfies a series of Gaussian distributions according to the central limit theorem. The underlying pandemic trend is first extracted using a singular spectral analysis (SSA) technique, which decomposes the NDCC time series into the sum of a small number of independent and interpretable components such as a slow varying trend, oscillatory components and structureless noise. We then use a mixture of Gaussian fitting (GF) to derive a novel predictive model for the SSA extracted NDCC incidence trend, with the overall model termed SSA-GF. Our proposed model is shown to accurately predict the NDCC trend, peak daily cases, the length of the pandemic period, the total confirmed cases and the associated dates of the turning points on the cumulated NDCC curve. Further, the three key model parameters, specifically, the amplitude ( alpha ), mean ( mu ), and standard deviation ( sigma ) are linked to the underlying pandemic patterns, and enable a directly interpretable evaluation of the impact of NPIs, such as strict lockdowns and travel restrictions. The predictive model is validated using available data from China and South Korea, and new predictions are made, partially requiring future validation, for the cases of Italy, Spain, the UK and the USA. Comparative results demonstrate that the introduction of consistent control measures across countries can lead to development of similar parametric models, reflected in particular by relative variations in their underlying sigma, alpha and mu values. The paper concludes with a number of open questions and outlines future research directions.

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JMLR Journal 2020 Journal Article

Minimax Nonparametric Parallelism Test

Xin Xing
Meimei Liu
Ping Ma
Wenxuan Zhong

Testing the hypothesis of parallelism is a fundamental statistical problem arising from many applied sciences. In this paper, we develop a nonparametric parallelism test for inferring whether the trends are parallel in treatment and control groups. In particular, the proposed nonparametric parallelism test is a Wald type test based on a smoothing spline ANOVA (SSANOVA) model which can characterize the complex patterns of the data. We derive that the asymptotic null distribution of the test statistic is a Chi-square distribution, unveiling a new version of Wilks phenomenon. Notably, we establish the minimax sharp lower bound of the distinguishable rate for the nonparametric parallelism test by using the information theory, and further prove that the proposed test is minimax optimal. Simulation studies are conducted to investigate the empirical performance of the proposed test. DNA methylation and neuroimaging studies are presented to illustrate potential applications of the test. The software is available at https://github.com/BioAlgs/Parallelism. [abs] [ pdf ][ bib ] [ code ] &copy JMLR 2020. ( edit, beta )

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NeurIPS Conference 2020 Conference Paper

Sufficient dimension reduction for classification using principal optimal transport direction

Cheng Meng
Jun Yu
Jingyi Zhang
Ping Ma
Wenxuan Zhong

Sufficient dimension reduction is used pervasively as a supervised dimension reduction approach. Most existing sufficient dimension reduction methods are developed for data with a continuous response and may have an unsatisfactory performance for the categorical response, especially for the binary-response. To address this issue, we propose a novel estimation method of sufficient dimension reduction subspace (SDR subspace) using optimal transport. The proposed method, named principal optimal transport direction (POTD), estimates the basis of the SDR subspace using the principal directions of the optimal transport coupling between the data respecting different response categories. The proposed method also reveals the relationship among three seemingly irrelevant topics, i. e. , sufficient dimension reduction, support vector machine, and optimal transport. We study the asymptotic properties of POTD and show that in the cases when the class labels contain no error, POTD estimates the SDR subspace exclusively. Empirical studies show POTD outperforms most of the state-of-the-art linear dimension reduction methods.

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NeurIPS Conference 2019 Conference Paper

Large-scale optimal transport map estimation using projection pursuit

Cheng Meng
Yuan Ke
Jingyi Zhang
Mengrui Zhang
Wenxuan Zhong
Ping Ma

This paper studies the estimation of large-scale optimal transport maps (OTM), which is a well known challenging problem owing to the curse of dimensionality. Existing literature approximates the large-scale OTM by a series of one-dimensional OTM problems through iterative random projection. Such methods, however, suffer from slow or none convergence in practice due to the nature of randomly selected projection directions. Instead, we propose an estimation method of large-scale OTM by combining the idea of projection pursuit regression and sufficient dimension reduction. The proposed method, named projection pursuit Monge map (PPMM), adaptively selects the most informative'' projection direction in each iteration. We theoretically show the proposed dimension reduction method can consistently estimate the most informative'' projection direction in each iteration. Furthermore, the PPMM algorithm weakly convergences to the target large-scale OTM in a reasonable number of steps. Empirically, PPMM is computationally easy and converges fast. We assess its finite sample performance through the applications of Wasserstein distance estimation and generative models.

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JMLR Journal 2015 Journal Article

A Statistical Perspective on Algorithmic Leveraging

Ping Ma
Michael W. Mahoney
Bin Yu

One popular method for dealing with large-scale data sets is sampling. For example, by using the empirical statistical leverage scores as an importance sampling distribution, the method of algorithmic leveraging samples and rescales rows/columns of data matrices to reduce the data size before performing computations on the subproblem. This method has been successful in improving computational efficiency of algorithms for matrix problems such as least-squares approximation, least absolute deviations approximation, and low-rank matrix approximation. Existing work has focused on algorithmic issues such as worst-case running times and numerical issues associated with providing high-quality implementations, but none of it addresses statistical aspects of this method. In this paper, we provide a simple yet effective framework to evaluate the statistical properties of algorithmic leveraging in the context of estimating parameters in a linear regression model with a fixed number of predictors. In particular, for several versions of leverage-based sampling, we derive results for the bias and variance, both conditional and unconditional on the observed data. We show that from the statistical perspective of bias and variance, neither leverage-based sampling nor uniform sampling dominates the other. This result is particularly striking, given the well-known result that, from the algorithmic perspective of worst-case analysis, leverage-based sampling provides uniformly superior worst-case algorithmic results, when compared with uniform sampling. Based on these theoretical results, we propose and analyze two new leveraging algorithms: one constructs a smaller least-squares problem with "shrinkage" leverage scores (SLEV), and the other solves a smaller and unweighted (or biased) least-squares problem (LEVUNW). A detailed empirical evaluation of existing leverage-based methods as well as these two new methods is carried out on both synthetic and real data sets. The empirical results indicate that our theory is a good predictor of practical performance of existing and new leverage- based algorithms and that the new algorithms achieve improved performance. For example, with the same computation reduction as in the original algorithmic leveraging approach, our proposed SLEV typically leads to improved biases and variances both unconditionally and conditionally (on the observed data), and our proposed LEVUNW typically yields improved unconditional biases and variances. [abs] [ pdf ][ bib ] &copy JMLR 2015. ( edit, beta )

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