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Yuguang Yan

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16 papers

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AAAI Conference 2025 Conference Paper

Hypergraph Learning for Unsupervised Graph Alignment via Optimal Transport

Yuguang Yan
Canlin Yang
Yuanlin Chen
Ruichu Cai
Michael Ng

Unsupervised graph alignment aims to find corresponding nodes across different graphs without supervision. Existing methods usually leverage the graph structure to aggregate features of nodes to find relations between nodes. However, the graph structure is inherently limited in pairwise relations between nodes without considering higher-order dependencies among multiple nodes. In this paper, we take advantage of the hypergraph structure to characterize higher-order structural information among nodes for better graph alignment. Specifically, we propose an optimal transport model to learn a hypergraph to capture complex relations among nodes, so that the nodes involved in one hyperedge can be adaptively based on local geometric information. In addition, inspired by the Dirichlet energy function of a hypergraph, we further refine our model to enhance the consistency between structural and feature information in each hyperedge. After that, we jointly leverage graphs and hypergraphs to extract structural and feature information to better model the relations between nodes, which is used to find node correspondences across graphs. We conduct experiments on several benchmark datasets with different settings, and the results demonstrate the effectiveness of our proposed method.

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ICLR Conference 2025 Conference Paper

Improving Deep Regression with Tightness

Shihao Zhang
Yuguang Yan
Angela Yao

For deep regression, preserving the ordinality of the targets with respect to the feature representation improves performance across various tasks. However, a theoretical explanation for the benefits of ordinality is still lacking. This work reveals that preserving ordinality reduces the conditional entropy $H(Z|Y)$ of representation $Z$ conditional on the target $Y$. However, our findings reveal that typical regression losses fail to sufficiently reduce $H(Z|Y)$, despite its crucial role in generalization performance. With this motivation, we introduce an optimal transport-based regularizer to preserve the similarity relationships of targets in the feature space to reduce $H(Z|Y)$. Additionally, we introduce a simple yet efficient strategy of duplicating the regressor targets, also with the aim of reducing $H(Z|Y)$. Experiments on three real-world regression tasks verify the effectiveness of our strategies to improve deep regression. Code: https://github.com/needylove/Regression_tightness

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ICML Conference 2025 Conference Paper

Reducing Confounding Bias without Data Splitting for Causal Inference via Optimal Transport

Yuguang Yan
Zongyu Li
Haolin Yang
Zeqin Yang
Hao Zhou
Ruichu Cai
Zhifeng Hao

Causal inference seeks to estimate the effect given a treatment such as a medicine or the dosage of a medication. To reduce the confounding bias caused by the non-randomized treatment assignment, most existing methods reduce the shift between subpopulations receiving different treatments. However, these methods split limited training samples into smaller groups, which cuts down the number of samples in each group, while precise distribution estimation and alignment highly rely on a sufficient number of training samples. In this paper, we propose a distribution alignment paradigm without data splitting, which can be naturally applied in the settings of binary and continuous treatments. To this end, we characterize the confounding bias by considering different probability measures of the same set including all the training samples, and exploit the optimal transport theory to analyze the confounding bias and outcome estimation error. Based on this, we propose to learn balanced representations by reducing the bias between the marginal distribution and the conditional distribution of a treatment. As a result, data reduction caused by splitting is avoided, and the outcome prediction model trained on one treatment group can be generalized to the entire population. The experiments on both binary and continuous treatment settings demonstrate the effectiveness of our method.

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AAAI Conference 2024 Conference Paper

An Optimal Transport View for Subspace Clustering and Spectral Clustering

Yuguang Yan
Zhihao Xu
Canlin Yang
Jie Zhang
Ruichu Cai
Michael Kwok-Po Ng

Clustering is one of the most fundamental problems in machine learning and data mining, and many algorithms have been proposed in the past decades. Among them, subspace clustering and spectral clustering are the most famous approaches. In this paper, we provide an explanation for subspace clustering and spectral clustering from the perspective of optimal transport. Optimal transport studies how to move samples from one distribution to another distribution with minimal transport cost, and has shown a powerful ability to extract geometric information. By considering a self optimal transport model with only one group of samples, we observe that both subspace clustering and spectral clustering can be explained in the framework of optimal transport, and the optimal transport matrix bridges the spaces of features and spectral embeddings. Inspired by this connection, we propose a spectral optimal transport barycenter model, which learns spectral embeddings by solving a barycenter problem equipped with an optimal transport discrepancy and guidance of data. Based on our proposed model, we take advantage of optimal transport to exploit both feature and metric information involved in data for learning coupled spectral embeddings and affinity matrix in a unified model. We develop an alternating optimization algorithm to solve the resultant problems, and conduct experiments in different settings to evaluate the performance of our proposed methods.

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IJCAI Conference 2024 Conference Paper

Combinatorial Routing for Neural Trees

Jiahao Li
Ruichu Cai
Yuguang Yan

Neural trees benefit from the high-level representation of neural networks and the interpretability of decision trees. Therefore, the existing works on neural trees perform outstandingly on various tasks such as architecture search. However, these works require every router to provide only one successor for each sample, causing the predictions to be dominated by the elite branch and its derivative architectures. To break this branch dominance, we propose the combinatorial routing neural tree method, termed CombRo. Unlike the previous methods employing unicast routing, CombRo performs multicast schema in each iteration, allowing the features to be routed to any combination of successors at every non-leaf. The weights of each architecture are then evaluated accordingly. We update the weights by training the routing subnetwork, and the architecture with the top weight is selected in the final step. We compare CombRo with the existing algorithms on 3 public image datasets, demonstrating its superior performance in terms of accuracy. Visualization results further validate the effectiveness of the multicast routing schema. Code is available at https: //github. com/JiahaoLi-gdut/CombRo.

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ICML Conference 2024 Conference Paper

Doubly Robust Causal Effect Estimation under Networked Interference via Targeted Learning

Weilin Chen 0001
Ruichu Cai
Zeqin Yang
Jie Qiao
Yuguang Yan
Zijian Li 0001
Zhifeng Hao

Causal effect estimation under networked interference is an important but challenging problem. Available parametric methods are limited in their model space, while previous semiparametric methods, e. g. , leveraging neural networks to fit only one single nuisance function, may still encounter misspecification problems under networked interference without appropriate assumptions on the data generation process. To mitigate bias stemming from misspecification, we propose a novel doubly robust causal effect estimator under networked interference, by adapting the targeted learning technique to the training of neural networks. Specifically, we generalize the targeted learning technique into the networked interference setting and establish the condition under which an estimator achieves double robustness. Based on the condition, we devise an end-to-end causal effect estimator by transforming the identified theoretical condition into a targeted loss. Moreover, we provide a theoretical analysis of our designed estimator, revealing a faster convergence rate compared to a single nuisance model. Extensive experimental results on two real-world networks with semisynthetic data demonstrate the effectiveness of our proposed estimators.

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AAAI Conference 2024 Conference Paper

Exploiting Geometry for Treatment Effect Estimation via Optimal Transport

Yuguang Yan
Zeqin Yang
Weilin Chen
Ruichu Cai
Zhifeng Hao
Michael Kwok-Po Ng

Estimating treatment effects from observational data suffers from the issue of confounding bias, which is induced by the imbalanced confounder distributions between the treated and control groups. As an effective approach, re-weighting learns a group of sample weights to balance the confounder distributions. Existing methods of re-weighting highly rely on a propensity score model or moment alignment. However, for complex real-world data, it is difficult to obtain an accurate propensity score prediction. Although moment alignment is free of learning a propensity score model, accurate estimation for high-order moments is computationally difficult and still remains an open challenge, and first and second-order moments are insufficient to align the distributions and easy to be misled by outliers. In this paper, we exploit geometry to capture the intrinsic structure involved in data for balancing the confounder distributions, so that confounding bias can be reduced even with outliers. To achieve this, we construct a connection between treatment effect estimation and optimal transport, a powerful tool to capture geometric information. After that, we propose an optimal transport model to learn sample weights by extracting geometry from confounders, in which geometric information between groups and within groups is leveraged for better confounder balancing. A projected mirror descent algorithm is employed to solve the derived optimization problem. Experimental studies on both synthetic and real-world datasets demonstrate the effectiveness of our proposed method.

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AAAI Conference 2024 Conference Paper

Hypergraph Joint Representation Learning for Hypervertices and Hyperedges via Cross Expansion

Yuguang Yan
Yuanlin Chen
Shibo Wang
Hanrui Wu
Ruichu Cai

Hypergraph captures high-order information in structured data and obtains much attention in machine learning and data mining. Existing approaches mainly learn representations for hypervertices by transforming a hypergraph to a standard graph, or learn representations for hypervertices and hyperedges in separate spaces. In this paper, we propose a hypergraph expansion method to transform a hypergraph to a standard graph while preserving high-order information. Different from previous hypergraph expansion approaches like clique expansion and star expansion, we transform both hypervertices and hyperedges in the hypergraph to vertices in the expanded graph, and construct connections between hypervertices or hyperedges, so that richer relationships can be used in graph learning. Based on the expanded graph, we propose a learning model to embed hypervertices and hyperedges in a joint representation space. Compared with the method of learning separate spaces for hypervertices and hyperedges, our method is able to capture common knowledge involved in hypervertices and hyperedges, and also improve the data efficiency and computational efficiency. To better leverage structure information, we minimize the graph reconstruction loss to preserve the structure information in the model. We perform experiments on both hypervertex classification and hyperedge classification tasks to demonstrate the effectiveness of our proposed method.

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ICML Conference 2024 Conference Paper

Reducing Balancing Error for Causal Inference via Optimal Transport

Yuguang Yan
Hao Zhou
Zeqin Yang
Weilin Chen 0001
Ruichu Cai
Zhifeng Hao

Most studies on causal inference tackle the issue of confounding bias by reducing the distribution shift between the control and treated groups. However, it remains an open question to adopt an appropriate metric for distribution shift in practice. In this paper, we define a generic balancing error on reweighted samples to characterize the confounding bias, and study the connection between the balancing error and the Wasserstein discrepancy derived from the theory of optimal transport. We not only regard the Wasserstein discrepancy as the metric of distribution shift, but also explore the association between the balancing error and the underlying cost function involved in the Wasserstein discrepancy. Motivated by this, we propose to reduce the balancing error under the framework of optimal transport with learnable marginal distributions and the cost function, which is implemented by jointly learning weights and representations associated with factual outcomes. The experiments on both synthetic and real-world datasets demonstrate the effectiveness of our proposed method.

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AAAI Conference 2024 Conference Paper

TNPAR: Topological Neural Poisson Auto-Regressive Model for Learning Granger Causal Structure from Event Sequences

Yuequn Liu
Ruichu Cai
Wei Chen
Jie Qiao
Yuguang Yan
Zijian Li
Keli Zhang
Zhifeng Hao

Learning Granger causality from event sequences is a challenging but essential task across various applications. Most existing methods rely on the assumption that event sequences are independent and identically distributed (i.i.d.). However, this i.i.d. assumption is often violated due to the inherent dependencies among the event sequences. Fortunately, in practice, we find these dependencies can be modeled by a topological network, suggesting a potential solution to the non-i.i.d. problem by introducing the prior topological network into Granger causal discovery. This observation prompts us to tackle two ensuing challenges: 1) how to model the event sequences while incorporating both the prior topological network and the latent Granger causal structure, and 2) how to learn the Granger causal structure. To this end, we devise a unified topological neural Poisson auto-regressive model with two processes. In the generation process, we employ a variant of the neural Poisson process to model the event sequences, considering influences from both the topological network and the Granger causal structure. In the inference process, we formulate an amortized inference algorithm to infer the latent Granger causal structure. We encapsulate these two processes within a unified likelihood function, providing an end-to-end framework for this task. Experiments on simulated and real-world data demonstrate the effectiveness of our approach.

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JBHI Journal 2021 Journal Article

Combating Ambiguity for Hash-Code Learning in Medical Instance Retrieval

Jiansheng Fang
Huazhu Fu
Dan Zeng
Xiao Yan
Yuguang Yan
Jiang Liu

When encountering a dubious diagnostic case, medical instance retrieval can help radiologists make evidence-based diagnoses by finding images containing instances similar to a query case from a large image database. The similarity between the query case and retrieved similar cases is determined by visual features extracted from pathologically abnormal regions. However, the manifestation of these regions often lacks specificity, i. e. , different diseases can have the same manifestation, and different manifestations may occur at different stages of the same disease. To combat the manifestation ambiguity in medical instance retrieval, we propose a novel deep framework called Y-Net, encoding images into compact hash-codes generated from convolutional features by feature aggregation. Y-Net can learn highly discriminative convolutional features by unifying the pixel-wise segmentation loss and classification loss. The segmentation loss allows exploring subtle spatial differences for good spatial-discriminability while the classification loss utilizes class-aware semantic information for good semantic-separability. As a result, Y-Net can enhance the visual features in pathologically abnormal regions and suppress the disturbing of the background during model training, which could effectively embed discriminative features into the hash-codes in the retrieval stage. Extensive experiments on two medical image datasets demonstrate that Y-Net can alleviate the ambiguity of pathologically abnormal regions and its retrieval performance outperforms the state-of-the-art method by an average of 9. 27% on the returned list of 10.

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TIST Journal 2020 Journal Article

Domain-attention Conditional Wasserstein Distance for Multi-source Domain Adaptation

Hanrui Wu
Yuguang Yan
Michael K. Ng
Qingyao Wu

Multi-source domain adaptation has received considerable attention due to its effectiveness of leveraging the knowledge from multiple related sources with different distributions to enhance the learning performance. One of the fundamental challenges in multi-source domain adaptation is how to determine the amount of knowledge transferred from each source domain to the target domain. To address this issue, we propose a new algorithm, called Domain-attention Conditional Wasserstein Distance (DCWD), to learn transferred weights for evaluating the relatedness across the source and target domains. In DCWD, we design a new conditional Wasserstein distance objective function by taking the label information into consideration to measure the distance between a given source domain and the target domain. We also develop an attention scheme to compute the transferred weights of different source domains based on their conditional Wasserstein distances to the target domain. After that, the transferred weights can be used to reweight the source data to determine their importance in knowledge transfer. We conduct comprehensive experiments on several real-world data sets, and the results demonstrate the effectiveness and efficiency of the proposed method.

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TIST Journal 2019 Journal Article

Online Heterogeneous Transfer Learning by Knowledge Transition

Hanrui Wu
Yuguang Yan
Yuzhong Ye
Huaqing Min
Michael K. Ng
Qingyao Wu

In this article, we study the problem of online heterogeneous transfer learning, where the objective is to make predictions for a target data sequence arriving in an online fashion, and some offline labeled instances from a heterogeneous source domain are provided as auxiliary data. The feature spaces of the source and target domains are completely different, thus the source data cannot be used directly to assist the learning task in the target domain. To address this issue, we take advantage of unlabeled co-occurrence instances as intermediate supplementary data to connect the source and target domains, and perform knowledge transition from the source domain into the target domain. We propose a novel online heterogeneous transfer learning algorithm called O nline H eterogeneous K nowledge T ransition (OHKT) for this purpose. In OHKT, we first seek to generate pseudo labels for the co-occurrence data based on the labeled source data, and then develop an online learning algorithm to classify the target sequence by leveraging the co-occurrence data with pseudo labels. Experimental results on real-world data sets demonstrate the effectiveness and efficiency of the proposed algorithm.

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AAAI Conference 2019 Conference Paper

Oversampling for Imbalanced Data via Optimal Transport

Yuguang Yan
Mingkui Tan
Yanwu Xu
Jiezhang Cao
Michael Ng
Huaqing Min
Qingyao Wu

The issue of data imbalance occurs in many real-world applications especially in medical diagnosis, where normal cases are usually much more than the abnormal cases. To alleviate this issue, one of the most important approaches is the oversampling method, which seeks to synthesize minority class samples to balance the numbers of different classes. However, existing methods barely consider global geometric information involved in the distribution of minority class samples, and thus may incur distribution mismatching between real and synthetic samples. In this paper, relying on optimal transport (Villani 2008), we propose an oversampling method by exploiting global geometric information of data to make synthetic samples follow a similar distribution to that of minority class samples. Moreover, we introduce a novel regularization based on synthetic samples and shift the distribution of minority class samples according to loss information. Experiments on toy and real-world data sets demonstrate the efficacy of our proposed method in terms of multiple metrics.

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IJCAI Conference 2018 Conference Paper

Semi-Supervised Optimal Transport for Heterogeneous Domain Adaptation

Yuguang Yan
Wen Li
Hanrui Wu
Huaqing Min
Mingkui Tan
Qingyao Wu

Heterogeneous domain adaptation (HDA) aims to exploit knowledge from a heterogeneous source domain to improve the learning performance in a target domain. Since the feature spaces of the source and target domains are different, the transferring of knowledge is extremely difficult. In this paper, we propose a novel semi-supervised algorithm for HDA by exploiting the theory of optimal transport (OT), a powerful tool originally designed for aligning two different distributions. To match the samples between heterogeneous domains, we propose to preserve the semantic consistency between heterogeneous domains by incorporating label information into the entropic Gromov-Wasserstein discrepancy, which is a metric in OT for different metric spaces, resulting in a new semi-supervised scheme. Via the new scheme, the target and transported source samples with the same label are enforced to follow similar distributions. Lastly, based on the Kullback-Leibler metric, we develop an efficient algorithm to optimize the resultant problem. Comprehensive experiments on both synthetic and real-world datasets demonstrate the effectiveness of our proposed method.

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IJCAI Conference 2017 Conference Paper

Learning Discriminative Correlation Subspace for Heterogeneous Domain Adaptation

Yuguang Yan
Wen Li
Michael Ng
Mingkui Tan
Hanrui Wu
Huaqing Min
Qingyao Wu

Domain adaptation aims to reduce the effort on collecting and annotating target data by leveraging knowledge from a different source domain. The domain adaptation problem will become extremely challenging when the feature spaces of the source and target domains are different, which is also known as the heterogeneous domain adaptation (HDA) problem. In this paper, we propose a novel HDA method to find the optimal discriminative correlation subspace for the source and target data. The discriminative correlation subspace is inherited from the canonical correlation subspace between the source and target data, and is further optimized to maximize the discriminative ability for the target domain classifier. We formulate a joint objective in order to simultaneously learn the discriminative correlation subspace and the target domain classifier. We then apply an alternating direction method of multiplier (ADMM) algorithm to address the resulting non-convex optimization problem. Comprehensive experiments on two real-world data sets demonstrate the effectiveness of the proposed method compared to the state-of-the-art methods.

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