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Ming Yu

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6 papers

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EAAI Journal 2025 Journal Article

A prior segmentation knowledge enhanced deep learning system for the classification of tumors in ultrasound image

Tao Jiang
Jun Guo
Wenyu Xing
Ming Yu
Yifang Li
Bo Zhang
Yi Dong
Dean Ta

Breast and thyroid cancers are prevalent among women worldwide. Ultrasound (US) examination is widely used for the early detection of breast and thyroid cancers. However, due to the blurred tumor boundaries and irregular shapes, the computer-aided diagnosis (CAD) of tumors based on US is challenging. Numerous studies have introduced deep learning-based multi-task learning approaches to address this issue, but these methods may result in feature redundancy and misinformation. Tumor segmentation is a prerequisite step for US CAD, and a higher Dice coefficient is associated with more accurate classification outcomes. Therefore, this paper introduces a novel deep-learning system that fully utilizes segmentation knowledge to boost classification performance. The system starts with a hybrid convolutional neural network (CNN)-Transformer for tumor localization and coarse segmentation, then uses a lightweight CNN-based U-Net to refine segmentation results. Subsequently, segmentation knowledge is harnessed to augment the network input and enhance multimodal feature extraction, resulting in improved classification performance. Our proposed method yielded a Dice coefficient of 83. 62% and 77. 20% for breast and thyroid tumor segmentation and area under curve (AUC) values of 0. 9536 and 0. 9475 for their respective classifications. Compared to non-segmentation knowledge-based classification models, our method obtained an increase in AUC of 0. 1054 and 0. 0566 on the breast and thyroid datasets, respectively. It outperformed the performance of the State-Of-The-Art (SOTA) methods across various datasets. In summary, our proposed system shows promise for application in US tumor analysis and holds potential to be extended to additional diseases and modalities.

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JMLR Journal 2020 Journal Article

Estimation of a Low-rank Topic-Based Model for Information Cascades

Ming Yu
Varun Gupta
Mladen Kolar

We consider the problem of estimating the latent structure of a social network based on the observed information diffusion events, or cascades, where the observations for a given cascade consist of only the timestamps of infection for infected nodes but not the source of the infection. Most of the existing work on this problem has focused on estimating a diffusion matrix without any structural assumptions on it. In this paper, we propose a novel model based on the intuition that an information is more likely to propagate among two nodes if they are interested in similar topics which are also prominent in the information content. In particular, our model endows each node with an influence vector (which measures how authoritative the node is on each topic) and a receptivity vector (which measures how susceptible the node is for each topic). We show how this node-topic structure can be estimated from the observed cascades, and prove the consistency of the estimator. Experiments on synthetic and real data demonstrate the improved performance and better interpretability of our model compared to existing state-of-the-art methods. [abs] [ pdf ][ bib ] [ code ] &copy JMLR 2020. ( edit, beta )

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JMLR Journal 2020 Journal Article

Simultaneous Inference for Pairwise Graphical Models with Generalized Score Matching

Ming Yu
Varun Gupta
Mladen Kolar

Probabilistic graphical models provide a flexible yet parsimonious framework for modeling dependencies among nodes in networks. There is a vast literature on parameter estimation and consistent model selection for graphical models. However, in many of the applications, scientists are also interested in quantifying the uncertainty associated with the estimated parameters and selected models, which current literature has not addressed thoroughly. In this paper, we propose a novel estimator for statistical inference on edge parameters in pairwise graphical models based on generalized Hyvarinen scoring rule. Hyvarinen scoring rule is especially useful in cases where the normalizing constant cannot be obtained efficiently in a closed form, which is a common problem for graphical models, including Ising models and truncated Gaussian graphical models. Our estimator allows us to perform statistical inference for general graphical models whereas the existing works mostly focus on statistical inference for Gaussian graphical models where finding normalizing constant is computationally tractable. Under mild conditions that are typically assumed in the literature for consistent estimation, we prove that our proposed estimator is $\sqrt{n}$-consistent and asymptotically normal, which allows us to construct confidence intervals and build hypothesis tests for edge parameters. Moreover, we show how our proposed method can be applied to test hypotheses that involve a large number of model parameters simultaneously. We illustrate validity of our estimator through extensive simulation studies on a diverse collection of data-generating processes. [abs] [ pdf ][ bib ] &copy JMLR 2020. ( edit, beta )

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NeurIPS Conference 2019 Conference Paper

Convergent Policy Optimization for Safe Reinforcement Learning

Ming Yu
Zhuoran Yang
Mladen Kolar
Zhaoran Wang

We study the safe reinforcement learning problem with nonlinear function approximation, where policy optimization is formulated as a constrained optimization problem with both the objective and the constraint being nonconvex functions. For such a problem, we construct a sequence of surrogate convex constrained optimization problems by replacing the nonconvex functions locally with convex quadratic functions obtained from policy gradient estimators. We prove that the solutions to these surrogate problems converge to a stationary point of the original nonconvex problem. Furthermore, to extend our theoretical results, we apply our algorithm to examples of optimal control and multi-agent reinforcement learning with safety constraints.

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NeurIPS Conference 2018 Conference Paper

Provable Gaussian Embedding with One Observation

Ming Yu
Zhuoran Yang
Tuo Zhao
Mladen Kolar
Zhaoran Wang

The success of machine learning methods heavily relies on having an appropriate representation for data at hand. Traditionally, machine learning approaches relied on user-defined heuristics to extract features encoding structural information about data. However, recently there has been a surge in approaches that learn how to encode the data automatically in a low dimensional space. Exponential family embedding provides a probabilistic framework for learning low-dimensional representation for various types of high-dimensional data. Though successful in practice, theoretical underpinnings for exponential family embeddings have not been established. In this paper, we study the Gaussian embedding model and develop the first theoretical results for exponential family embedding models. First, we show that, under a mild condition, the embedding structure can be learned from one observation by leveraging the parameter sharing between different contexts even though the data are dependent with each other. Second, we study properties of two algorithms used for learning the embedding structure and establish convergence results for each of them. The first algorithm is based on a convex relaxation, while the other solved the non-convex formulation of the problem directly. Experiments demonstrate the effectiveness of our approach.

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NeurIPS Conference 2016 Conference Paper

Statistical Inference for Pairwise Graphical Models Using Score Matching

Ming Yu
Mladen Kolar
Varun Gupta

Probabilistic graphical models have been widely used to model complex systems and aid scientific discoveries. As a result, there is a large body of literature focused on consistent model selection. However, scientists are often interested in understanding uncertainty associated with the estimated parameters, which current literature has not addressed thoroughly. In this paper, we propose a novel estimator for edge parameters for pairwise graphical models based on Hyv\"arinen scoring rule. Hyv\"arinen scoring rule is especially useful in cases where the normalizing constant cannot be obtained efficiently in a closed form. We prove that the estimator is $\sqrt{n}$-consistent and asymptotically Normal. This result allows us to construct confidence intervals for edge parameters, as well as, hypothesis tests. We establish our results under conditions that are typically assumed in the literature for consistent estimation. However, we do not require that the estimator consistently recovers the graph structure. In particular, we prove that the asymptotic distribution of the estimator is robust to model selection mistakes and uniformly valid for a large number of data-generating processes. We illustrate validity of our estimator through extensive simulation studies.

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