Author name cluster

Xueying Li

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

8 papers

1 author row

AAAI Conference 2026 Conference Paper

Content Diversity-guided Ambiguity Mitigation for Open-Set Noisy Label Learning

Zhihao Zhou
Rui Li
Xueying Li

Open-set noisy label learning faces a critical challenge in maintaining robust DNN performance when training data contain both in-distribution noisy (IDN) and out-of-distribution (OOD) samples. These noisy samples induce overconfident but erroneous predictions due to their ambiguous positions relative to category boundaries. Current methods address this by filtering noisy samples based on visual features alone, they fail to resolve the semantic ambiguity near decision boundaries, where limited visual cues lead to unreliable sample purification. To this end, we propose Content Diversity-guided Ambiguity Mitigation (CDgAM), a novel framework that leverages diverse contents to mitigate visual ambiguity in open-set noisy label learning. CDgAM leverages textual descriptions of intra-class commonality and inter-class disparity to dynamically refine semantic boundaries, reducing bias in prototype learning. To further suppress early-stage uncertainty in visual representations, we design a region-sensitive distillation regularization that transfers boundary-aware knowledge from a multimodal large language model to the target DNN. Extensive experiments conducted on various datasets with different noise levels demonstrate the effectiveness of our CDgAM, outperforming state-of-the-art methods for open-set noisy label learning.

PDF Details DOI

AAAI Conference 2026 Conference Paper

MoMoREC: A Multi-agent Motivation Generation Framework for Residual Semantic ID-Aware Recommendation

Yige Wang
Mingming Li
Li Wang
Kaichen Zhao
Wangming Li
Weipeng Jiang
Xueying Li

Recent advances in the field of sequential recommendation have highlighted the potential of Large Language Models (LLMs) in enhancing item embeddings and improving user understanding. However, existing approaches face three major limitations: 1) insufficient understanding of the reasons behind users' purchase decisions, 2) the high-dimensional embeddings directly produced by LLMs are not well compatible with traditional low-dimensional ID embeddings and 3) reliance on additional fine-tuning and high inference overhead to adapt LLMs to the recommendation task. In this paper, we propose MoMoREC, a simple yet effective user-understanding-based recommendation strategy. This method leverages the intrinsic comprehension capabilities of LLMs combined with residual semantic IDs to better understand users. Specifically, starting from common user purchasing behaviors and incorporating item characteristics, we employ a multi-agent framework to utilize LLMs in analyzing user shopping motivations and extracting high-dimensional dense embeddings. These embeddings are then transformed into low-dimensional IDs using a residual semantic ID approach via clustering and residual dimensionality reduction, which can be fed into the recommendation model. MoMoREC effectively integrates the understanding power of LLMs with the strengths of recommendation systems, preserving rich semantic language embeddings while reducing or eliminating the need for auxiliary trainable modules. As a result, it seamlessly adapts to any sequential recommendation framework. Experiments on three benchmark datasets show that MoMoRec significantly improves traditional recommendation models, demonstrating its effectiveness and flexibility.

PDF Details DOI

JBHI Journal 2025 Journal Article

Effectiveness Evaluation for Clinical Depression Detection Using Deep Learning Based Synthetic House-Tree-Person Test

Zhuolong Chen
Xiaoqing Yin
Fan Yang
Xiaofan Li
Zixuan Zhao
Xueying Li
Jianghu Liu
Yubin Zhao

Depression is one of the most common mood disorders and the number of patients increases significantly in recent years. Due to the lack of biomarkers, conversation between patients and psychiatrists is still the main clinical diagnostic method which is easily influenced by subjectivity of both patients and psychiatrists. Synthetic House-tree-person test (S-HTP), a convenient and efficient mental assessment tool, minimizes subjective influences from patients, while its effectiveness is limited by the professional ability of analyst. Here we introduce a deep learning model DeHTP, a flexible and convenient depression detection method based on S-HTP without interaction between people. Experimental results demonstrate that DeHTP achieves 0. 963 AUC and 0. 9 accuracy, and outperforms the conventional manual analysis of S-HTP, which is conducted on the guideline of 50 conclusions from previous study related to depression. In addition, it reveals 22 depression-correlated drawing features aligned with conclusions above from the perspective of our proposed model. Leveraging the advantages of deep learning and S-HTP, this approach has the potential for widespread promotion and adoption as the available tool for daily self-mental monitoring, as well as the promising auxiliary diagnostic method in clinical.

Details DOI

JAIR Journal 2025 Journal Article

Practical Parallel Algorithms for Non-Monotone Submodular Maximization

Shuang Cui
Kai Han
Jing Tang
Xueying Li
Aakas Zhiyuli
Hanxiao Li

Submodular maximization has found extensive applications in various domains within the field of artificial intelligence, including but not limited to machine learning, computer vision, and natural language processing. With the increasing size of datasets in these domains, there is a pressing need to develop efficient and parallelizable algorithms for submodular maximization. One measure of the parallelizability of a submodular maximization algorithm is its adaptive complexity, which indicates the number of sequential rounds where a polynomial number of queries to the objective function can be executed in parallel. In this paper, we study the problem of non-monotone submodular maximization subject to a knapsack constraint, and propose a low-adaptivity algorithm achieving an (1/8 − ϵ)- approximation with practical Õ(n) query complexity. Moreover, we also propose the first algorithm with both provable approximation ratio and sublinear adaptive complexity for the problem of non-monotone submodular maximization subject to a k-system constraint. As a by-product, we show that our two algorithms can also be applied to the special case of submodular maximization subject to a cardinality constraint, and achieve performance bounds comparable with those of state-of-the-art algorithms. Finally, the effectiveness of our algorithms is demonstrated by extensive experiments on real-world applications.

PDF Details DOI

YNIMG Journal 2025 Journal Article

Tasting emotions: An in-depth fmri study exploring gustatory and visual cross-modal associations across various spatio-temporal regions of the human brain

Jie Chen
Yuansheng Liu
Lina Huang
Luming Hu
Xueying Li
Liuqing Wei
Weiping Yang
Simin Zhao

This study investigates how taste influences emotional face recognition, focusing on the cross-modal interaction between gustatory and visual stimuli. While prior research has primarily examined how visual cues modulate taste perception, the reverse direction-how taste shapes visual processing in emotional contexts-remains underexplored. Using a combination of task-based functional MRI (task-fMRI) and resting-state fMRI (rs-fMRI), we examined the neural mechanisms by which taste modulates the perception of emotional faces. Behaviorally, sour tastes facilitated faster recognition of disgusted faces, while sweet tastes enhanced the detection of pleasant expressions. Neuroimaging results revealed that these emotionally congruent taste-face pairings elicited distinct activation patterns in the early visual cortex, including a significant interaction effect in the right calcarine gyrus (primary visual cortex, V1). Task-fMRI also showed modulation in the medial cingulate gyrus, fusiform gyrus, and superior frontal regions depending on emotional congruency. Resting-state fMRI revealed sustained alterations in intrinsic connectivity within the medial cingulate and paracingulate cortex following cross-modal dissonance, suggesting lasting neural effects beyond stimulus presentation. Together, these findings demonstrate the dynamic and enduring influence of taste on emotional face processing and offer novel insights into the neural basis of multisensory affective integration. By integrating task-based and resting-state fMRI, this study provides a comprehensive framework for understanding how affectively salient gustatory inputs shape social perception through both early perceptual and sustained neural mechanisms.

Details DOI

AAAI Conference 2023 Conference Paper

Practical Parallel Algorithms for Submodular Maximization Subject to a Knapsack Constraint with Nearly Optimal Adaptivity

Shuang Cui
Kai Han
Jing Tang
He Huang
Xueying Li
Aakas Zhiyuli

Submodular maximization has wide applications in machine learning and data mining, where massive datasets have brought the great need for designing efficient and parallelizable algorithms. One measure of the parallelizability of a submodular maximization algorithm is its adaptivity complexity, which indicates the number of sequential rounds where a polynomial number of queries to the objective function can be executed in parallel. In this paper, we study the problem of non-monotone submodular maximization subject to a knapsack constraint, and propose the first combinatorial algorithm achieving an (8+epsilon)-approximation under O(log n) adaptive complexity, which is optimal up to a factor of O(loglog n). Moreover, under slightly larger adaptivity, we also propose approximation algorithms with nearly optimal query complexity of O(n), while achieving better approximation ratios. We show that our algorithms can also be applied to the special case of submodular maximization subject to a cardinality constraint, and achieve performance bounds comparable with those of state-of-the-art algorithms. Finally, the effectiveness of our approach is demonstrated by extensive experiments on real-world applications.

PDF Details DOI

NeurIPS Conference 2021 Conference Paper

BCORLE($\lambda$): An Offline Reinforcement Learning and Evaluation Framework for Coupons Allocation in E-commerce Market

Yang Zhang
Bo Tang
Qingyu Yang
Dou An
Hongyin Tang
Chenyang Xi
Xueying Li
Feiyu Xiong

Coupons allocation is an important tool for enterprises to increase the activity and loyalty of users on the e-commerce market. One fundamental problem related is how to allocate coupons within a fixed budget while maximizing users' retention on the e-commerce platform. The online e-commerce environment is complicated and ever changing, so it requires the coupons allocation policy learning can quickly adapt to the changes of the company's business strategy. Unfortunately, existing studies with a huge computation overhead can hardly satisfy the requirements of real-time and fast-response in the real world. Specifically, the problem of coupons allocation within a fixed budget is usually formulated as a Lagrangian problem. Existing solutions need to re-learn the policy once the value of Lagrangian multiplier variable $\lambda$ is updated, causing a great computation overhead. Besides, a mature e-commerce market often faces tens of millions of users and dozens of types of coupons which construct the huge policy space, further increasing the difficulty of solving the problem. To tackle with above problems, we propose a budget constrained offline reinforcement learning and evaluation with $\lambda$-generalization (BCORLE($\lambda$)) framework. The proposed method can help enterprises develop a coupons allocation policy which greatly improves users' retention rate on the platform while ensuring the cost does not exceed the budget. Specifically, $\lambda$-generalization method is proposed to lead the policy learning process can be executed according to different $\lambda$ values adaptively, avoiding re-learning new polices from scratch. Thus the computation overhead is greatly reduced. Further, a novel offline reinforcement learning method and an off-policy evaluation algorithm are proposed for policy learning and policy evaluation, respectively. Finally, experiments on the simulation platform and real-world e-commerce market validate the effectiveness of our approach.

PDF Details

TIST Journal 2012 Journal Article

Learning to Infer the Status of Heavy-Duty Sensors for Energy-Efficient Context-Sensing

Xueying Li
Huanhuan Cao
Enhong Chen
Jilei Tian

With the prevalence of smart mobile devices with multiple sensors, the commercial application of intelligent context-aware services becomes more and more attractive. However, limited by the battery capacity, the energy efficiency of context-sensing is the bottleneck for the success of context-aware applications. Though several previous studies for energy-efficient context-sensing have been reported, none of them can be applied to multiple types of high-energy-consuming sensors. Moreover, applying machine learning technologies to energy-efficient context-sensing is underexplored too. In this article, we propose to leverage machine learning technologies for improving the energy efficiency of multiple high-energy-consuming context sensors by trading off the sensing accuracy. To be specific, we try to infer the status of high-energy-consuming sensors according to the outputs of software-based sensors and the physical sensors that are necessary to work all the time for supporting the basic functions of mobile devices. If the inference indicates the high-energy-consuming sensor is in a stable status, we avoid the unnecessary invocation and instead use the latest invoked value as the estimation. The experimental results on real datasets show that the energy efficiency of GPS sensing and audio-level sensing are significantly improved by the proposed approach while the sensing accuracy is over 90%.

Details DOI