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Yuan Guo

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5 papers

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NeurIPS Conference 2025 Conference Paper

LBMKGC: Large Model-Driven Balanced Multimodal Knowledge Graph Completion

Yuan Guo
Qian Ma
Hui Li
Qiao Ning
Furui Zhan
Yu Gu
Ge Yu
Shikai Guo

Multi-modal Knowledge Graph Completion (MMKGC) aims to predict missing entities, relations, or attributes in knowledge graphs by collaboratively modeling the triple structure and multimodal information (e. g. , text, images, videos) associated with entities. This approach facilitates the automatic discovery of previously unobserved factual knowledge. However, existing MMKGC methods encounter several critical challenges: (i) the imbalance of inter-entity information across different modalities; (ii) the heterogeneity of intra-entity multimodal information; and (iii) for a given entity, the informational contributions of different modalities are inconsistent across contexts. In this paper, we propose a novel L arge model-driven B alanced M ultimodal K nowledge G raph C ompletion framework, termed LBMKGC. Subsequently, to bridge the semantic gap between heterogeneous modalities, LBMKGC aligns the multimodal embeddings of entities semantically by using the CLIP (Contrastive Language-Image Pre-Training) model. Furthermore, LBMKGC adaptively fuses multimodal embeddings with relational guidance by distinguishing between the perceptual and conceptual attributes of triples. Finally, extensive experiments conducted against 21 state-of-the-art baselines demonstrate that LBMKGC achieves superior performance across diverse datasets and scenarios while maintaining efficiency and generalizability. Our code and data are publicly available at: https: //github. com/guoynow/LBMKGC.

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JBHI Journal 2025 Journal Article

Localized Intra- and Inter-Tumoral Heterogeneity for Predicting Treatment Response to Neoadjuvant Chemotherapy in Breast Cancer

Yinhao Liang
Wenjie Tang
Qingcong Kong
Ting Wang
Jianjun Zhang
Wing W. Y. Ng
Siyi Chen
Ying Li

This study proposes a novel method for extracting breast cancer tumor heterogeneity descriptors to non-invasively predict whether pathological complete response (pCR) can be achieved after neoadjuvant chemotherapy (NAC). These localized descriptors extract corresponding heterogeneity features for different radiomic features and are able to capture tumor characteristics at various localization levels. These descriptors also capture tumor heterogeneity both at the individual tumor level and across the whole dataset, providing decision-making models with features that are both more effective and interpretable. We validated the effectiveness of the proposed features with the Kolmogorov-Arnold network (KAN) across multiple centers, yielding an AUC of 0. 92 when combined with pathological features and demonstrating good performance in external datasets (AUCs of 0. 84 and 0. 81). Additionally, we transform the best model into a symbolic formula to intuitively explain the machine learning model's prediction process, showing how factors such as age, HER2, Ki-67 and heterogeneity influence the prediction. The symbolized model is consistent with the experience of clinical experts, which enhances users' confidence in deep models. The experimental results show that our proposed features and method outperform classical heterogeneity features and end-to-end neural networks with a small additional computational cost.

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JBHI Journal 2025 Journal Article

XRadNet: A Radiomics-Guided Breast Cancer Molecular Subtype Prediction Network With a Radiomics Explanation

Yinhao Liang
Wenjie Tang
Jianjun Zhang
Ting Wang
Wing W. Y. Ng
Siyi Chen
Kuiming Jiang
Xinhua Wei

In this work, we propose a radiomics-guided neural network, XRadNet, for breast cancer molecular subtype prediction. XRadNet is a two-head neural network, with one for predicting molecular subtypes and the other for approximating radiomic features. In addition, a training scheme with radiomics guidance is proposed to improve performance. First, we conduct a series of experiments to test the radiomic feature learning capacity of different neural networks, which determines the backbone of XRadNet. Moreover, significant radiomic features are also determined according to radiomics and prior knowledge. XRadNet is subsequently pretrained in a self-supervised manner. The pretraining uses synthetic samples to train the backbone and radiomic feature regression head. This mitigates the impact of an insufficient number of samples. Finally, XRadNet is fine-tuned with a downstream real-world dataset by enabling all heads. Furthermore, a logistic regression is built with radiomic features and learned features, which provides a new way to interpreting the trained model with concepts familiar to radiologists. The experimental results show that XRadNet effectively predicts the four molecular subtypes of breast cancer. These results also demonstrate that the proposed training scheme yields better or competitive performance than those models pretrained on ImageNet or medical datasets.

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IJCAI Conference 2024 Conference Paper

Optimal Auction Design with User Coupons in Advertising Systems

Xiaodong Liu
Zhikang Fan
Yiming Ding
Yuan Guo
Lihua Zhang
Changcheng Li
Dongying Kong
Han Li

Online advertising is a major revenue source for most Internet companies. The advertising opportunities are usually sold to advertisers through auctions that take into account the bids of the advertisers and the click-through rates (CTRs) and the conversion rates (CVRs) of the users. Standard auction design theory perceives both the CTRs and the CVRs as constants. We consider a new auction mechanism that offers coupons to users when displaying the ads. Such coupons allow the user to buy the advertisers' products or services at a lower price, which increases both the CTRs and the CVRs of the ads. In this paper, we formulate the problem mathematically and perform a systematic analysis. We characterize the set of individually rational and incentive compatible mechanisms in our setting. Based on the characterization, we identify the optimal strategy of offering coupons that maximizes the platform's expected revenue. We also conduct extensive experiments on both synthetic data and industrial data. Our experiment results show that our mechanism significantly improves both the revenue and welfare of the platform, thereby creating a win-win situation for all parties including the platform, the advertisers, and the user.

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IJCAI Conference 2018 Conference Paper

Experimental Design under the Bradley-Terry Model

Yuan Guo
Peng Tian
Jayashree Kalpathy-Cramer
Susan Ostmo
J. Peter Campbell
Michael F. Chiang
Deniz Erdogmus
Jennifer Dy

Labels generated by human experts via comparisons exhibit smaller variance compared to traditional sample labels. Collecting comparison labels is challenging over large datasets, as the number of comparisons grows quadratically with the dataset size. We study the following experimental design problem: given a budget of expert comparisons, and a set of existing sample labels, we determine the comparison labels to collect that lead to the highest classification improvement. We study several experimental design objectives motivated by the Bradley-Terry model. The resulting optimization problems amount to maximizing submodular functions. We experimentally evaluate the performance of these methods over synthetic and real-life datasets.

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