Wei Pan

EAAI Journal 2026 Journal Article

An improved graph attention network for semantic segmentation of industrial point clouds in automotive battery sealing nail defect detection

• Wei Pan
• Yuhao Wu
• Wenming Tang
• Qinghua Lu
• Yunzhi Zhang

Accurate defect detection in automotive battery sealing nails is vital for safety and reliability. Traditional methods combine two-dimensional (2D) vision for localization with three-dimensional (3D) vision for measurement, resulting in complex workflows and reduced efficiency. We propose Local Graph Attention for Semantic Segmentation (LGASS), an end-to-end 3D point cloud segmentation model. LGASS processes raw point cloud data from structured-light systems, performing simultaneous defect localization and geometric quantification in a single stage. By leveraging a graph attention mechanism in an encoder–decoder architecture, LGASS captures local geometric features and long-range dependencies, excelling on industrial metallic surfaces. Experiments show LGASS achieves 99. 47% Overall Accuracy (OA), 92. 37% mean Accuracy (mAcc), and 79. 23% mean Intersection over Union (mIoU), offering a robust solution for automated sealing nail inspection.

ICML Conference 2025 Conference Paper

DiLQR: Differentiable Iterative Linear Quadratic Regulator via Implicit Differentiation

• Shuyuan Wang
• Philip D. Loewen
• Michael G. Forbes
• R. Bhushan Gopaluni
• Wei Pan

While differentiable control has emerged as a powerful paradigm combining model-free flexibility with model-based efficiency, the iterative Linear Quadratic Regulator (iLQR) remains underexplored as a differentiable component. The scalability of differentiating through extended iterations and horizons poses significant challenges, hindering iLQR from being an effective differentiable controller. This paper introduces DiLQR, a framework that facilitates differentiation through iLQR, allowing it to serve as a trainable and differentiable module, either as or within a neural network. A novel aspect of this framework is the analytical solution that it provides for the gradient of an iLQR controller through implicit differentiation, which ensures a constant backward cost regardless of iteration, while producing an accurate gradient. We evaluate our framework on imitation tasks on famous control benchmarks. Our analytical method demonstrates superior computational performance, achieving up to $\textbf{128x}$ speedup and a minimum of $\textbf{21x}$ speedup compared to automatic differentiation. Our method also demonstrates superior learning performance ($\mathbf{10^6x}$) compared to traditional neural network policies and better model loss with differentiable controllers that lack exact analytical gradients. Furthermore, we integrate our module into a larger network with visual inputs to demonstrate the capacity of our method for high-dimensional, fully end-to-end tasks. Codes can be found on the project homepage https: //sites. google. com/view/dilqr/.

NeurIPS Conference 2025 Conference Paper

Spatial-Aware Decision-Making with Ring Attractors in Reinforcement Learning Systems

• Marcos Negre Saura
• Richard Allmendinger
• Wei Pan
• Theodore Papamarkou

Ring attractors, mathematical models inspired by neural circuit dynamics, provide a biologically plausible mechanism to improve learning speed and accuracy in Reinforcement Learning (RL). Serving as specialized brain-inspired structures that encode spatial information and uncertainty, ring attractors explicitly encode the action space, facilitate the organization of neural activity, and enable the distribution of spatial representations across the neural network in the context of Deep Reinforcement Learning (DRL). These structures also provide temporal filtering that stabilizes action selection during exploration, for example, by preserving the continuity between rotation angles in robotic control or adjacency between tactical moves in game-like environments. The application of ring attractors in the action selection process involves mapping actions to specific locations on the ring and decoding the selected action based on neural activity. We investigate the application of ring attractors by both building an exogenous model and integrating them as part of DRL agents. Our approach significantly improves state-of-the-art performance on the Atari 100k benchmark, achieving a 53\% increase in performance over selected baselines.

EAAI Journal 2024 Journal Article

A conditional generative model for end-to-end stress field prediction of composite bolted joints

• Yong Zhao
• Yuming Liu
• Qingyuan Lin
• Wei Pan
• Wencai Yu
• Yu Ren
• Sheng Liu

Carbon Fiber Reinforced Polymer (CFRP) laminates, prized for their lightweight and high stiffness, are extensively used in aerospace and maritime applications. Bolted joints play a crucial role in connecting these laminates. However, manufacturing variations arise during the assembly process, impacting performance due to material-related factors. Predicting the assembly stress fields of Carbon Fiber Reinforced Polymer bolted joints is of great significance in design optimization, manufacturing process control, and structural health monitoring. The currently prevalent finite element analysis methods incur extremely high computational costs, failing to meet the requirements for real-time prediction of the assembly and multiparametric design of composite bolted joints. Proposing a methodological framework for rapidly predicting the assembly physical field is necessary. This paper introduces a stress prediction framework to enhance analysis and aid material parameter design. The framework is inspired by image processing and artificial intelligence drawing by analogizing the computed physical field results to the generated images. Therefore, the Bolted Tightening Generative Adversarial Network (BT-GAN), a cascaded generative model, is proposed in this paper to predict stress fields of the composite bolted joints during assembly. The model starts with data augmentation of the stress filed results from the finite element analysis in a super-resolution network, which realizes an integral interpolation mapping from coarse-grid to fine-grid results. Then, the results of the data enhancement are fed into the subsequent conditional generative adversarial network for learning. Similar to the text-guided image generation approach, the network learns to understand the physical mapping relationships between different parameters and assembly stress fields. Moreover, the network achieves higher accuracy in stress field prediction by extraction the understanding of multi-scale features through the skip connection and the attention mechanism. This method effectively learns the physical mapping relationship between multiple parameters and the stress field, applying a graph generation approach to end-to-end predictions of the field. Compared to the results of finite element analysis from the coarse-grid, the Structure Similarity Index Measure (SSIM) of the cascaded generative network proposed in this paper has been improved from 0. 584 to 0. 962 and the Peak Signal-to-Noise Ratio (PSNR) metric has been increased from 17. 3 dB to 58. 2 dB. What's more, the mean relative error on the maximum values of the stress field has reached 6. 9%. The trained model takes only 6. 1s to complete a single prediction, significantly improving the prediction efficiency compared with finite element analysis. It is compared with the other networks commonly used for physical field prediction and shows improvement in the metrics proposed in the article. By constructing such an end-to-end stress field prediction framework during assembly, efficient forecasting for the assembly of composite bolted joints can be achieved. This is advantageous for the digital twin modeling of the assembly lines and the effective control of assembly quality, providing a powerful tool for assembly design and analysis.

NeurIPS Conference 2024 Conference Paper

Aligning Individual and Collective Objectives in Multi-Agent Cooperation

• Yang Li
• Wenhao Zhang
• Jianhong Wang
• Shao Zhang
• Yali Du
• Ying Wen
• Wei Pan

Among the research topics in multi-agent learning, mixed-motive cooperation is one of the most prominent challenges, primarily due to the mismatch between individual and collective goals. The cutting-edge research is focused on incorporating domain knowledge into rewards and introducing additional mechanisms to incentivize cooperation. However, these approaches often face shortcomings such as the effort on manual design and the absence of theoretical groundings. To close this gap, we model the mixed-motive game as a differentiable game for the ease of illuminating the learning dynamics towards cooperation. More detailed, we introduce a novel optimization method named \textbf{\textit{A}}ltruistic \textbf{\textit{G}}radient \textbf{\textit{A}}djustment (\textbf{\textit{AgA}}) that employs gradient adjustments to progressively align individual and collective objectives. Furthermore, we theoretically prove that AgA effectively attracts gradients to stable fixed points of the collective objective while considering individual interests, and we validate these claims with empirical evidence. We evaluate the effectiveness of our algorithm AgA through benchmark environments for testing mixed-motive collaboration with small-scale agents such as the two-player public good game and the sequential social dilemma games, Cleanup and Harvest, as well as our self-developed large-scale environment in the game StarCraft II.

JMLR Journal 2024 Journal Article

Causal Discovery with Generalized Linear Models through Peeling Algorithms

• Minjie Wang
• Xiaotong Shen
• Wei Pan

This article presents a novel method for causal discovery with generalized structural equation models suited for analyzing diverse types of outcomes, including discrete, continuous, and mixed data. Causal discovery often faces challenges due to unmeasured confounders that hinder the identification of causal relationships. The proposed approach addresses this issue by developing two peeling algorithms (bottom-up and top-down) to ascertain causal relationships and valid instruments. This approach first reconstructs a super-graph to represent ancestral relationships between variables, using a peeling algorithm based on nodewise GLM regressions that exploit relationships between primary and instrumental variables. Then, it estimates parent-child effects from the ancestral relationships using another peeling algorithm while deconfounding a child's model with information borrowed from its parents' models. The article offers a theoretical analysis of the proposed approach, establishing conditions for model identifiability and providing statistical guarantees for accurately discovering parent-child relationships via the peeling algorithms. Furthermore, the article presents numerical experiments showcasing the effectiveness of our approach in comparison to state-of-the-art structure learning methods without confounders. Lastly, it demonstrates an application to Alzheimer's disease (AD), highlighting the method's utility in constructing gene-to-gene and gene-to-disease regulatory networks involving Single Nucleotide Polymorphisms (SNPs) for healthy and AD subjects. [abs] [ pdf ][ bib ] [ code ] &copy JMLR 2024. ( edit, beta )

JAIR Journal 2024 Journal Article

Tackling Cooperative Incompatibility for Zero-Shot Human-AI Coordination

• Yang Li
• Shao Zhang
• Jichen Sun
• Wenhao Zhang
• Yali Du
• Ying Wen
• Xinbing Wang
• Wei Pan

Securing coordination between AI agent and teammates (human players or AI agents) in contexts involving unfamiliar humans continues to pose a significant challenge in Zero-Shot Coordination. The issue of cooperative incompatibility becomes particularly prominent when an AI agent is unsuccessful in synchronizing with certain previously unknown partners. Traditional algorithms have aimed to collaborate with partners by optimizing fixed objectives within a population, fostering diversity in strategies and behaviors. However, these techniques may lead to learning loss and an inability to cooperate with specific strategies within the population, a phenomenon named cooperative incompatibility in learning. In order to solve cooperative incompatibility in learning and effectively address the problem in the context of ZSC, we introduce the Cooperative Open-ended LEarning (COLE) framework, which formulates open-ended objectives in cooperative games with two players using perspectives of graph theory to evaluate and pinpoint the cooperative capacity of each strategy. We present two practical algorithms, specifically COLESV and COLER, which incorporate insights from game theory and graph theory. We also show that COLE could effectively overcome the cooperative incompatibility from theoretical and empirical analysis. Subsequently, we created an online Overcooked human-AI experiment platform, the COLE platform, which enables easy customization of questionnaires, model weights, and other aspects. Utilizing the COLE platform, we enlist 130 participants for human experiments. Our findings reveal a preference for our approach over state-of-the-art methods using a variety of subjective metrics. Moreover, objective experimental outcomes in the Overcooked game environment indicate that our method surpasses existing ones when coordinating with previously unencountered AI agents and the human proxy model. Our code and demo are publicly available at https://sites.google.com/view/cole-2023.

EAAI Journal 2023 Journal Article

A combination-based machine learning algorithm estimating impacts of social, economic, and environmental on resident health—on China’s provincial panel data

• Li Wen
• Wei Pan
• Shujie Liao
• Wulin Pan
• Hui Xu
• Cheng Hu

The factors influencing residents health have become complex and intertwined with the development of economy and society. Traditional research with a single factor on health will not provide an accurate picture of the situation. This paper collects data on economic, environmental and social factors to estimate their impact on regional health. Considering the data is multi-source and complex, this paper proposes a combined feature importance algorithm, which weighted the feature importance of RF, XGB and SOIL. The algorithm does not depend on the data and adaptively approximates the true results. The results show that economic factors have a significant and direct impact on health, environmental factors have a lag correlation with health level, and social factors have a more complicated effect on health. Finally, we provide policy suggestions for health on economic, environmental, and social development.

JMLR Journal 2023 Journal Article

Inference for a Large Directed Acyclic Graph with Unspecified Interventions

• Chunlin Li
• Xiaotong Shen
• Wei Pan

Statistical inference of directed relations given some unspecified interventions (i.e., the intervention targets are unknown) is challenging. In this article, we test hypothesized directed relations with unspecified interventions. First, we derive conditions to yield an identifiable model. Unlike classical inference, testing directed relations requires identifying the ancestors and relevant interventions of hypothesis-specific primary variables. To this end, we propose a peeling algorithm based on nodewise regressions to establish a topological order of primary variables. Moreover, we prove that the peeling algorithm yields a consistent estimator in low-order polynomial time. Second, we propose a likelihood ratio test integrated with a data perturbation scheme to account for the uncertainty of identifying ancestors and interventions. Also, we show that the distribution of a data perturbation test statistic converges to the target distribution. Numerical examples demonstrate the utility and effectiveness of the proposed methods, including an application to infer gene regulatory networks. [abs] [ pdf ][ bib ] [ code ] &copy JMLR 2023. ( edit, beta )

TMLR Journal 2023 Journal Article

JiangJun: Mastering Xiangqi by Tackling Non-Transitivity in Two-Player Zero-Sum Games

• Yang Li
• Kun Xiong
• Yingping Zhang
• Jiangcheng Zhu
• Stephen Marcus McAleer
• Wei Pan
• Jun Wang
• Zonghong Dai

This paper presents an empirical exploration of non-transitivity in perfect-information games, specifically focusing on Xiangqi, a traditional Chinese board game comparable in game-tree complexity to chess and shogi. By analyzing over 10,000 records of human Xiangqi play, we highlight the existence of both transitive and non-transitive elements within the game’s strategic structure. To address non-transitivity, we introduce the JiangJun algorithm, an innovative combination of Monte-Carlo Tree Search (MCTS) and Policy Space Response Oracles (PSRO) designed to approximate a Nash equilibrium. We evaluate the algorithm empirically using a WeChat mini program and achieve a Master level with a 99.41% win rate against human players. The algorithm’s effectiveness in overcoming non-transitivity is confirmed by a plethora of metrics, such as relative population performance and visualization results. Our project site is available at https://sites.google.com/view/jiangjun-site/.

JMLR Journal 2020 Journal Article

A Regularization-Based Adaptive Test for High-Dimensional GLMs

• Chong Wu
• Gongjun Xu
• Xiaotong Shen
• Wei Pan

In spite of its urgent importance in the era of big data, testing high-dimensional parameters in generalized linear models (GLMs) in the presence of high-dimensional nuisance parameters has been largely under-studied, especially with regard to constructing powerful tests for general (and unknown) alternatives. Most existing tests are powerful only against certain alternatives and may yield incorrect Type 1 error rates under high-dimensional nuisance parameter situations. In this paper, we propose the adaptive interaction sum of powered score (aiSPU) test in the framework of penalized regression with a non-convex penalty, called truncated Lasso penalty (TLP), which can maintain correct Type 1 error rates while yielding high statistical power across a wide range of alternatives. To calculate its p-values analytically, we derive its asymptotic null distribution. Via simulations, its superior finite-sample performance is demonstrated over several representative existing methods. In addition, we apply it and other representative tests to an Alzheimer's Disease Neuroimaging Initiative (ADNI) data set, detecting possible gene-gender interactions for Alzheimer's disease. We also put R package “aispu” implementing the proposed test on GitHub. [abs] [ pdf ][ bib ] [ code ] &copy JMLR 2020. ( edit, beta )

YNIMG Journal 2020 Journal Article

Implicating causal brain imaging endophenotypes in Alzheimer’s disease using multivariable IWAS and GWAS summary data

• Katherine A. Knutson
• Yangqing Deng
• Wei Pan

Recent evidence suggests the existence of many undiscovered heritable brain phenotypes involved in Alzheimer’s Disease (AD) pathogenesis. This finding necessitates methods for the discovery of causal brain changes in AD that integrate Magnetic Resonance Imaging measures and genotypic data. However, existing approaches for causal inference in this setting, such as the univariate Imaging Wide Association Study (UV-IWAS), suffer from inconsistent effect estimation and inflated Type I errors in the presence of genetic pleiotropy, the phenomenon in which a variant affects multiple causal intermediate risk phenotypes. In this study, we implement a multivariate extension to the IWAS model, namely MV-IWAS, to consistently estimate and test for the causal effects of multiple brain imaging endophenotypes from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) in the presence of pleiotropic and possibly correlated SNPs. We further extend MV-IWAS to incorporate variant-specific direct effects on AD, analogous to the existing Egger regression Mendelian Randomization approach, which allows for testing of remaining pleiotropy after adjusting for multiple intermediate pathways. We propose a convenient approach for implementing MV-IWAS that solely relies on publicly available GWAS summary data and a reference panel. Through simulations with either individual-level or summary data, we demonstrate the well controlled Type I errors and superior power of MV-IWAS over UV-IWAS in the presence of pleiotropic SNPs. We apply the summary statistic based tests to 1578 heritable imaging derived phenotypes (IDPs) from the UK Biobank. MV-IWAS detected numerous IDPs as possible false positives by UV-IWAS while uncovering many additional causal neuroimaging phenotypes in AD which are strongly supported by the existing literature.

YNIMG Journal 2017 Journal Article

Imaging-wide association study: Integrating imaging endophenotypes in GWAS

• Zhiyuan Xu
• Chong Wu
• Wei Pan

A new and powerful approach, called imaging-wide association study (IWAS), is proposed to integrate imaging endophenotypes with GWAS to boost statistical power and enhance biological interpretation for GWAS discoveries. IWAS extends the promising transcriptome-wide association study (TWAS) from using gene expression endophenotypes to using imaging and other endophenotypes with a much wider range of possible applications. As illustration, we use gray-matter volumes of several brain regions of interest (ROIs) drawn from the ADNI-1 structural MRI data as imaging endophenotypes, which are then applied to the individual-level GWAS data of ADNI-GO/2 and a large meta-analyzed GWAS summary statistics dataset (based on about 74, 000 individuals), uncovering some novel genes significantly associated with Alzheimer's disease (AD). We also compare the performance of IWAS with TWAS, showing much larger numbers of significant AD-associated genes discovered by IWAS, presumably due to the stronger link between brain atrophy and AD than that between gene expression of normal individuals and the risk for AD. The proposed IWAS is general and can be applied to other imaging endophenotypes, and GWAS individual-level or summary association data.

NeurIPS Conference 2017 Conference Paper

Towards Accurate Binary Convolutional Neural Network

• Xiaofan Lin
• Cong Zhao
• Wei Pan

We introduce a novel scheme to train binary convolutional neural networks (CNNs) -- CNNs with weights and activations constrained to {-1, +1} at run-time. It has been known that using binary weights and activations drastically reduce memory size and accesses, and can replace arithmetic operations with more efficient bitwise operations, leading to much faster test-time inference and lower power consumption. However, previous works on binarizing CNNs usually result in severe prediction accuracy degradation. In this paper, we address this issue with two major innovations: (1) approximating full-precision weights with the linear combination of multiple binary weight bases; (2) employing multiple binary activations to alleviate information loss. The implementation of the resulting binary CNN, denoted as ABC-Net, is shown to achieve much closer performance to its full-precision counterpart, and even reach the comparable prediction accuracy on ImageNet and forest trail datasets, given adequate binary weight bases and activations.

JMLR Journal 2016 Journal Article

A New Algorithm and Theory for Penalized Regression-based Clustering

• Chong Wu
• Sunghoon Kwon
• Xiaotong Shen
• Wei Pan

Clustering is unsupervised and exploratory in nature. Yet, it can be performed through penalized regression with grouping pursuit, as demonstrated in Pan et al. (2013). In this paper, we develop a more efficient algorithm for scalable computation and a new theory of clustering consistency for the method. This algorithm, called DC-ADMM, combines difference of convex (DC) programming with the alternating direction method of multipliers (ADMM). This algorithm is shown to be more computationally efficient than the quadratic penalty based algorithm of Pan et al. (2013) because of the former's closed-form updating formulas. Numerically, we compare the DC- ADMM algorithm with the quadratic penalty algorithm to demonstrate its utility and scalability. Theoretically, we establish a finite-sample mis- clustering error bound for penalized regression based clustering with the $L_0$ constrained regularization in a general setting. On this ground, we provide conditions for clustering consistency of the penalized clustering method. As an end product, we put R package prclust implementing PRclust with various loss and grouping penalty functions available on GitHub and CRAN. [abs] [ pdf ][ bib ] &copy JMLR 2016. ( edit, beta )

YNIMG Journal 2015 Journal Article

A cautionary note on using secondary phenotypes in neuroimaging genetic studies

• Junghi Kim
• Wei Pan

Almost all genome-wide association studies (GWASs), including Alzheimer's Disease Neuroimaging Initiative (ADNI), are based on the case–control study design, implying that the resulting case–control data are likely a biased, not random, sample of the target population. Although association analysis of the disease (e. g. Alzheimer's disease in the ADNI) can be conducted using a standard logistic regression by ignoring the biased case–control sampling, a standard linear regression analysis on a secondary phenotype (e. g. any neuroimaging phenotype in the ADNI) may in general lead to biased inference, including biased parameter estimates, inflated Type I errors and reduced power for association testing. Despite of this well known result in genetic epidemiology, to our surprise, all the published studies on secondary phenotypes with the ADNI data have ignored this potential problem. Here we aim to answer whether such a standard analysis of a secondary phenotype is valid or problematic with the ADNI data. Through both real data analyses and simulation studies, we found that, strikingly, such an analysis was generally valid (with only small biases or slightly inflated Type I errors) for the ADNI data, though cautions must be taken when analyzing other data. We also illustrate applications and possible problems of two methods specifically developed for valid analysis of secondary phenotypes.

YNICL Journal 2015 Journal Article

Highly adaptive tests for group differences in brain functional connectivity

• Junghi Kim
• Wei Pan

Resting-state functional magnetic resonance imaging (rs-fMRI) and other technologies have been offering evidence and insights showing that altered brain functional networks are associated with neurological illnesses such as Alzheimer's disease. Exploring brain networks of clinical populations compared to those of controls would be a key inquiry to reveal underlying neurological processes related to such illnesses. For such a purpose, group-level inference is a necessary first step in order to establish whether there are any genuinely disrupted brain subnetworks. Such an analysis is also challenging due to the high dimensionality of the parameters in a network model and high noise levels in neuroimaging data. We are still in the early stage of method development as highlighted by Varoquaux and Craddock (2013) that "there is currently no unique solution, but a spectrum of related methods and analytical strategies" to learn and compare brain connectivity. In practice the important issue of how to choose several critical parameters in estimating a network, such as what association measure to use and what is the sparsity of the estimated network, has not been carefully addressed, largely because the answers are unknown yet. For example, even though the choice of tuning parameters in model estimation has been extensively discussed in the literature, as to be shown here, an optimal choice of a parameter for network estimation may not be optimal in the current context of hypothesis testing. Arbitrarily choosing or mis-specifying such parameters may lead to extremely low-powered tests. Here we develop highly adaptive tests to detect group differences in brain connectivity while accounting for unknown optimal choices of some tuning parameters. The proposed tests combine statistical evidence against a null hypothesis from multiple sources across a range of plausible tuning parameter values reflecting uncertainty with the unknown truth. These highly adaptive tests are not only easy to use, but also high-powered robustly across various scenarios. The usage and advantages of these novel tests are demonstrated on an Alzheimer's disease dataset and simulated data.

YNIMG Journal 2014 Journal Article

Comparison of statistical tests for group differences in brain functional networks

• Junghi Kim
• Jeffrey R. Wozniak
• Bryon A. Mueller
• Xiaotong Shen
• Wei Pan

Brain functional connectivity has been studied by analyzing time series correlations in regional brain activities based on resting-state fMRI data. Brain functional connectivity can be depicted as a network or graph defined as a set of nodes linked by edges. Nodes represent brain regions and an edge measures the strength of functional correlation between two regions. Most of existing work focuses on estimation of such a network. A key but inadequately addressed question is how to test for possible differences of the networks between two subject groups, say between healthy controls and patients. Here we illustrate and compare the performance of several state-of-the-art statistical tests drawn from the neuroimaging, genetics, ecology and high-dimensional data literatures. Both real and simulated data were used to evaluate the methods. We found that Network Based Statistic (NBS) performed well in many but not all situations, and its performance critically depends on the choice of its threshold parameter, which is unknown and difficult to choose in practice. Importantly, two adaptive statistical tests called adaptive sum of powered score (aSPU) and its weighted version (aSPUw) are easy to use and complementary to NBS, being higher powered than NBS in some situations. The aSPU and aSPUw tests can also be applied to adjust for covariates. Between the aSPU and aSPUw tests, they often, but not always, performed similarly with neither one as a uniform winner. On the other hand, Multivariate Matrix Distance Regression (MDMR) has been applied to detect group differences for brain connectivity; with the usual choice of the Euclidean distance, MDMR is a special case of the aSPU test. Consequently NBS, aSPU and aSPUw tests are recommended to test for group differences in functional connectivity.

YNIMG Journal 2014 Journal Article

Testing for association with multiple traits in generalized estimation equations, with application to neuroimaging data

• Yiwei Zhang
• Zhiyuan Xu
• Xiaotong Shen
• Wei Pan

There is an increasing need to develop and apply powerful statistical tests to detect multiple traits–single locus associations, as arising from neuroimaging genetics and other studies. For example, in the Alzheimer's Disease Neuroimaging Initiative (ADNI), in addition to genome-wide single nucleotide polymorphisms (SNPs), thousands of neuroimaging and neuropsychological phenotypes as intermediate phenotypes for Alzheimer's disease, have been collected. Although some classic methods like MANOVA and newly proposed methods may be applied, they have their own limitations. For example, MANOVA cannot be applied to binary and other discrete traits. In addition, the relationships among these methods are not well understood. Importantly, since these tests are not data adaptive, depending on the unknown association patterns among multiple traits and between multiple traits and a locus, these tests may or may not be powerful. In this paper we propose a class of data-adaptive weights and the corresponding weighted tests in the general framework of generalized estimation equations (GEE). A highly adaptive test is proposed to select the most powerful one from this class of the weighted tests so that it can maintain high power across a wide range of situations. Our proposed tests are applicable to various types of traits with or without covariates. Importantly, we also analytically show relationships among some existing and our proposed tests, indicating that many existing tests are special cases of our proposed tests. Extensive simulation studies were conducted to compare and contrast the power properties of various existing and our new methods. Finally, we applied the methods to an ADNI dataset to illustrate the performance of the methods. We conclude with the recommendation for the use of the GEE-based Score test and our proposed adaptive test for their high and complementary performance.

JMLR Journal 2013 Journal Article

Cluster Analysis: Unsupervised Learning via Supervised Learning with a Non-convex Penalty

• Wei Pan
• Xiaotong Shen
• Binghui Liu

Clustering analysis is widely used in many fields. Traditionally clustering is regarded as unsupervised learning for its lack of a class label or a quantitative response variable, which in contrast is present in supervised learning such as classification and regression. Here we formulate clustering as penalized regression with grouping pursuit. In addition to the novel use of a non-convex group penalty and its associated unique operating characteristics in the proposed clustering method, a main advantage of this formulation is its allowing borrowing some well established results in classification and regression, such as model selection criteria to select the number of clusters, a difficult problem in clustering analysis. In particular, we propose using the generalized cross-validation (GCV) based on generalized degrees of freedom (GDF) to select the number of clusters. We use a few simple numerical examples to compare our proposed method with some existing approaches, demonstrating our method's promising performance. [abs] [ pdf ][ bib ] &copy JMLR 2013. ( edit, beta )

AAAI Conference 2011 Conference Paper

Composite Social Network for Predicting Mobile Apps Installation

• Wei Pan
• Nadav Aharony
• Alex Pentland

We have carefully instrumented a large portion of the population living in a university graduate dormitory by giving participants Android smart phones running our sensing software. In this paper, we propose the novel problem of predicting mobile application (known as “apps”) installation using social networks and explain its challenge. Modern smart phones, like the ones used in our study, are able to collect different social networks using built-in sensors. (e. g. Bluetooth proximity network, call log network, etc) While this information is accessible to app market makers such as the iPhone AppStore, it has not yet been studied how app market makers can use these information for marketing research and strategy development. We develop a simple computational model to better predict app installation by using a composite network computed from the different networks sensed by phones. Our model also captures individual variance and exogenous factors in app adoption. We show the importance of considering all these factors in predicting app installations, and we observe the surprising result that app installation is indeed predictable. We also show that our model achieves the best results compared with generic approaches.

JMLR Journal 2011 Journal Article

Large Margin Hierarchical Classification with Mutually Exclusive Class Membership

• Huixin Wang
• Xiaotong Shen
• Wei Pan

In hierarchical classification, class labels are structured, that is each label value corresponds to one non-root node in a tree, where the inter-class relationship for classification is specified by directed paths of the tree. In such a situation, the focus has been on how to leverage the inter-class relationship to enhance the performance of flat classification, which ignores such dependency. This is critical when the number of classes becomes large relative to the sample size. This paper considers single-path or partial-path hierarchical classification, where only one path is permitted from the root to a leaf node. A large margin method is introduced based on a new concept of generalized margins with respect to hierarchy. For implementation, we consider support vector machines and ψ-learning. Numerical and theoretical analyses suggest that the proposed method achieves the desired objective and compares favorably against strong competitors in the literature, including its flat counterparts. Finally, an application to gene function prediction is discussed. [abs] [ pdf ][ bib ] &copy JMLR 2011. ( edit, beta )

JMLR Journal 2009 Journal Article

On Efficient Large Margin Semisupervised Learning: Method and Theory

• Junhui Wang
• Xiaotong Shen
• Wei Pan

In classification, semisupervised learning usually involves a large amount of unlabeled data with only a small number of labeled data. This imposes a great challenge in that it is difficult to achieve good classification performance through labeled data alone. To leverage unlabeled data for enhancing classification, this article introduces a large margin semisupervised learning method within the framework of regularization, based on an efficient margin loss for unlabeled data, which seeks efficient extraction of the information from unlabeled data for estimating the Bayes decision boundary for classification. For implementation, an iterative scheme is derived through conditional expectations. Finally, theoretical and numerical analyses are conducted, in addition to an application to gene function prediction. They suggest that the proposed method enables to recover the performance of its supervised counterpart based on complete data in rates of convergence, when possible. [abs] [ pdf ][ bib ] &copy JMLR 2009. ( edit, beta )

ICML Conference 2009 Conference Paper

Unsupervised hierarchical modeling of locomotion styles

• Wei Pan
• Lorenzo Torresani

JMLR Journal 2007 Journal Article

Penalized Model-Based Clustering with Application to Variable Selection

• Wei Pan
• Xiaotong Shen

Variable selection in clustering analysis is both challenging and important. In the context of model-based clustering analysis with a common diagonal covariance matrix, which is especially suitable for "high dimension, low sample size" settings, we propose a penalized likelihood approach with an L 1 penalty function, automatically realizing variable selection via thresholding and delivering a sparse solution. We derive an EM algorithm to fit our proposed model, and propose a modified BIC as a model selection criterion to choose the number of components and the penalization parameter. A simulation study and an application to gene function prediction with gene expression profiles demonstrate the utility of our method. [abs] [ pdf ][ bib ] &copy JMLR 2007. ( edit, beta )