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Runze Wu

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17 papers

2 author rows

YNIMG Journal 2026 Journal Article

Standardized quantification of [18F]Florbetazine amyloid PET with the Centiloid scale

Meiqi Wu
Menglin Liang
Chenhui Mao
Liling Dong
Qi Ge
Yuying Li
Jingnan Wang
Chao Ren

C]PiB across different image-processing pipelines and effective image resolutions (EIRs). METHODS: C]PiB SUVR were evaluated under different EIRs. RESULTS: F]FBZ SUVR were observed across EIRs with the SPM pipeline, whereas regression parameters varied across EIRs with the FreeSurfer pipeline. CONCLUSION: F]FBZ demonstrated equal or improved quantification precision, supporting its broader use in clinical and research Aβ imaging.

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AAAI Conference 2024 Conference Paper

EnMatch: Matchmaking for Better Player Engagement via Neural Combinatorial Optimization

Kai Wang
Haoyu Liu
Zhipeng Hu
Xiaochuan Feng
Minghao Zhao
Shiwei Zhao
Runze Wu
Xudong Shen

Matchmaking is a core task in e-sports and online games, as it contributes to player engagement and further influences the game's lifecycle. Previous methods focus on creating fair games at all times. They divide players into different tiers based on skill levels and only select players from the same tier for each game. Though this strategy can ensure fair matchmaking, it is not always good for player engagement. In this paper, we propose a novel Engagement-oriented Matchmaking (EnMatch) framework to ensure fair games and simultaneously enhance player engagement. Two main issues need to be addressed. First, it is unclear how to measure the impact of different team compositions and confrontations on player engagement during the game considering the variety of player characteristics. Second, such a detailed consideration on every single player during matchmaking will result in an NP-hard combinatorial optimization problem with non-linear objectives. In light of these challenges, we turn to real-world data analysis to reveal engagement-related factors. The resulting insights guide the development of engagement modeling, enabling the estimation of quantified engagement before a match is completed. To handle the combinatorial optimization problem, we formulate the problem into a reinforcement learning framework, in which a neural combinatorial optimization problem is built and solved. The performance of EnMatch is finally demonstrated through the comparison with other state-of-the-art methods based on several real-world datasets and online deployments on two games.

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YNIMG Journal 2024 Journal Article

Evaluation of a novel PET tracer [18F]-Florbetazine for Alzheimer's disease diagnosis and β-amyloid deposition quantification

Meiqi Wu
Chao Ren
Chenhui Mao
Liling Dong
Bo Li
Xueqian Yang
Zhenghai Huang
Haiqiong Zhang

F]-92) is a selective PET tracer for β-amyloid (Aβ) depositions with a novel diaryl-azine scaffold to reduce lipophilicity and to achieve higher gray-to-white matter contrast. We aimed to assess its diagnostic value in Alzheimer's disease (AD) and pharmacokinetics characteristics in human subjects. METHODS: F]-Florbetazine and a structural MRI scan. The time-activity-curves (TACs) for volumes of interest (VOIs) in cerebral cortex, cerebellar cortex and cerebral white matter was depicted and their standardized uptake value ratios (SUVRs) with cerebellar cortex as reference were compared between HCs and AD patients. The cerebral gray-to-white matter SUV ratio (GWR) was also calculated. RESULTS: In HCs, radioactivities in the cerebral cortex VOIs were homogeneously low and at the same level as in cerebellar cortex, while in AD patients, cortical VOIs expected to contain Aβ exhibited high radioactivity. Cerebral cortex SUVRs remain relatively low in HCs while keep increasing along with time in AD patients. After 15 min, the cerebral cortex SUVRs became significant higher in AD patients compared to HCs with 100 % discrimination accuracy. In AD patients, GWR remained over 1.3 for all time intervals and visual inspection showed lower uptake in cerebral white matter compared to cerebral cortex. CONCLUSION: F]-Florbetazine can be potentially used for detection and quantification of Aβ depositions in the living human brain.

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TIST Journal 2024 Journal Article

MHANER: A Multi-source Heterogeneous Graph Attention Network for Explainable Recommendation in Online Games

Dongjin Yu
Xingliang Wang
Yu Xiong
Xudong Shen
Runze Wu
Dongjing Wang
Zhene Zou
Guandong Xu

Recommender system helps address information overload problem and satisfy consumers’ personalized requirement in many applications such as e-commerce, social networks, and in-game store. However, existing approaches mainly focus on improving the accuracy of recommendation tasks but usually ignore how to improve the interpretability of recommendation, which is still a challenging and crucial task, especially for some complicated scenarios such as large-scale online games. A few previous attempts on explainable recommendation mostly depend on a large amount of a priori knowledge or user-provided review corpus, which is labor consuming as well as often suffers from data deficiency. To relieve this issue, we propose a Multi-source Heterogeneous Graph Attention Network for Explainable Recommendation (MHANER) for the case without enough a priori knowledge or corpus of user comments. Specifically, MHANER employs the attention mechanism to model players’ preference to in-game store items as the support for the explanation of recommendation. Then a graph neural network–based method is designed to model players’ multi-source heterogeneous information, including the players’ historical behavior data, historical purchase data, and attributes of the player-controlled character, which is leveraged to recommend possible items for players to buy. Finally, the multi-level subgraph pattern mining is adopted to combine the characteristics of a recommendation list to generate corresponding explanations of items. Extensive experiments on three real-world datasets, two collected from JD and one from NetEase game, demonstrate that the proposed model MHANER outperforms state-of-the-art baselines. Moreover, the generated explanations are verified by human encoding comprised of hard-core game players and endorsed by experts from game developers.

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ICML Conference 2024 Conference Paper

Towards Realistic Model Selection for Semi-supervised Learning

Muyang Li
Xiaobo Xia
Runze Wu
Fengming Huang
Jun Yu 0001
Bo Han 0003
Tongliang Liu

Semi-supervised Learning (SSL) has shown remarkable success in applications with limited supervision. However, due to the scarcity of labels in the training process, SSL algorithms are known to be impaired by the lack of proper model selection, as splitting a validation set will further reduce the limited labeled data, and the size of the validation set could be too small to provide a reliable indication to the generalization error. Therefore, we seek alternatives that do not rely on validation data to probe the generalization performance of SSL models. Specifically, we find that the distinct margin distribution in SSL can be effectively utilized in conjunction with the model’s spectral complexity, to provide a non-vacuous indication of the generalization error. Built upon this, we propose a novel model selection method, specifically tailored for SSL, known as S pectral-normalized La beled-margin M inimization (SLAM). We prove that the model selected by SLAM has upper-bounded differences w. r. t. the best model within the search space. In addition, comprehensive experiments showcase that SLAM can achieve significant improvements compared to its counterparts, verifying its efficacy from both theoretical and empirical standpoints.

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AAMAS Conference 2023 Conference Paper

Adaptive Value Decomposition with Greedy Marginal Contribution Computation for Cooperative Multi-Agent Reinforcement Learning

Shanqi Liu
Yujing Hu
Runze Wu
Dong Xing
Yu Xiong
Changjie Fan
Kun Kuang
Yong Liu

Real-world cooperation often requires intensive coordination among agents simultaneously. This task has been extensively studied within the framework of cooperative multi-agent reinforcement learning (MARL), and value decomposition methods are among those cuttingedge solutions. However, traditional methods that learn the value function as a monotonic mixing of per-agent utilities cannot solve the tasks with non-monotonic returns. This hinders their application in generic scenarios. Recent methods tackle this problem from the perspective of implicit credit assignment by learning value functions with complete expressiveness or using additional structures to improve cooperation. However, they are either difficult to learn due to large joint action spaces or insufficient to capture the complicated interactions among agents which are essential to solving tasks with non-monotonic returns. Moreover, applications in real-world scenarios usually require policies to be interpretable, but interpretability is limited in the implicit credit assignment methods. To address these problems, we propose a novel explicit credit assignment method to address the non-monotonic problem. Our method, Adaptive Value decomposition with Greedy Marginal contribution (AVGM), is based on an adaptive value decomposition that learns the cooperative value of a group of dynamically changing agents. We first illustrate that the proposed value decomposition can consider the complicated interactions among agents and is feasible to learn in large-scale scenarios. Then, our method uses a greedy marginal contribution computed from the value decomposition as an individual credit to incentivize agents to learn the optimal cooperative policy. We further extend the module with an action encoder to guarantee the linear time complexity for computing the greedy marginal contribution. Experimental results demonstrate that our method achieves significant performance improvements Proc. of the 22nd International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2023), A. Ricci, W. Yeoh, N. Agmon, B. An (eds.), May 29 – June 2, 2023, London, United Kingdom. © 2023 International Foundation for Autonomous Agents and Multiagent Systems (www. ifaamas. org). All rights reserved. in several non-monotonic domains. Besides, we showcase that our model maintains a good sense of interpretability and rationality. This suggests our model can be applied to scenarios with more realistic demands.

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NeurIPS Conference 2023 Conference Paper

InstanT: Semi-supervised Learning with Instance-dependent Thresholds

Muyang Li
Runze Wu
Haoyu Liu
Jun Yu
Xun Yang
Bo Han
Tongliang Liu

Semi-supervised learning (SSL) has been a fundamental challenge in machine learning for decades. The primary family of SSL algorithms, known as pseudo-labeling, involves assigning pseudo-labels to confident unlabeled instances and incorporating them into the training set. Therefore, the selection criteria of confident instances are crucial to the success of SSL. Recently, there has been growing interest in the development of SSL methods that use dynamic or adaptive thresholds. Yet, these methods typically apply the same threshold to all samples, or use class-dependent thresholds for instances belonging to a certain class, while neglecting instance-level information. In this paper, we propose the study of instance-dependent thresholds, which has the highest degree of freedom compared with existing methods. Specifically, we devise a novel instance-dependent threshold function for all unlabeled instances by utilizing their instance-level ambiguity and the instance-dependent error rates of pseudo-labels, so instances that are more likely to have incorrect pseudo-labels will have higher thresholds. Furthermore, we demonstrate that our instance-dependent threshold function provides a bounded probabilistic guarantee for the correctness of the pseudo-labels it assigns.

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IJCAI Conference 2023 Conference Paper

ProMix: Combating Label Noise via Maximizing Clean Sample Utility

Ruixuan Xiao
Yiwen Dong
Haobo Wang
Lei Feng
Runze Wu
Gang Chen
Junbo Zhao

Learning with Noisy Labels (LNL) has become an appealing topic, as imperfectly annotated data are relatively cheaper to obtain. Recent state-of-the-art approaches employ specific selection mechanisms to separate clean and noisy samples and then apply Semi-Supervised Learning (SSL) techniques for improved performance. However, the selection step mostly provides a medium-sized and decent-enough clean subset, which overlooks a rich set of clean samples. To fulfill this, we propose a novel LNL framework ProMix that attempts to maximize the utility of clean samples for boosted performance. Key to our method, we propose a matched high confidence selection technique that selects those examples with high confidence scores and matched predictions with given labels to dynamically expand a base clean sample set. To overcome the potential side effect of excessive clean set selection procedure, we further devise a novel SSL framework that is able to train balanced and unbiased classifiers on the separated clean and noisy samples. Extensive experiments demonstrate that ProMix significantly advances the current state-of-the-art results on multiple benchmarks with different types and levels of noise. It achieves an average improvement of 2. 48% on the CIFAR-N dataset.

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AAAI Conference 2022 Conference Paper

Co-promotion Predictions of Financing Market and Sales Market: A Cooperative-Competitive Attention Approach

Lei Zhang
Wang Xiang
Chuang Zhao
Hongke Zhao
Rui Li
Runze Wu

Market popularity prediction has always been a hot research topic, such as sales prediction and crowdfunding prediction. Most of these studies put the perspective on isolated markets, relying on the knowledge of certain market to maximize the prediction performance. However, these market-specific approaches are restricted by the knowledge limitation of isolated markets and incapable of the complicated and potential relations among different markets, especially some with strong dependence such as the financing market and sales market. Fortunately, we discover potentially symbiotic relations between the financing market and the sales market, which provides us with an opportunity to co-promote the popularity predictions of both markets. Thus, for bridgly learning the knowledge interactions between financing market and sales market, we propose a cross-market approach, namely CATN: Cooperative-competitive Attention Transfer Network, which could effectively transfer knowledge of financing capability from the crowdfunding market and sales prospect from the E-commerce market. Specifically, for capturing the complicated relations especially the cooperation or complement of items and enhancing the knowledge transfer between the two heterogeneous markets, we design a novel Cooperative Attention; meanwhile, for finely computing the relations of items especially the competition in specific same market, we further design Competitive Attentions for the two markets respectively. Besides, we also distinguish aligned features and unique features to adapt the cross-market predictions. With the real-world datasets collected from Indiegogo and Amazon, we construct extensive experiments on three types of datasets from the two markets and the results demonstrate the effectiveness and generalization of our CATN model.

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AAAI Conference 2022 System Paper

EasySM: A Data-Driven Intelligent Decision Support System for Server Merge

Manhu Qu
Jie Huang
Hao Deng
Runze Wu
Xudong Shen
Jianrong Tao
Tangjie Lv

As independent social economic entities, game servers play a dominant role in building a living and attractive virtual world in massive multi-player online role-playing games (MMORPGs). We propose and implement a novel intelligent decision support system for server merge (SM) which could benefit the maintaining of game ecology at the macro level. The services provided by the system consist of server health diagnosis, server merge assessment, and combination strategy recommendation. In particular, we design an effective time series prediction algorithm to diagnose the health status of one server (e. g. , player activity) based on real game scenarios, and then select the servers with poor status from all servers. Moreover, to dig out the inherent development laws of servers from the historical merge records, we leverage a correlation measurement algorithm to find the historical merged servers that are similar to the servers to be merged and then evaluate the potential trend after merging, which can assist experts to make reasonable decisions. We deploy our system online for multiple MMORPGs and achieve sound online performance endorsed by the game operation team.

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IJCAI Conference 2022 Conference Paper

MLP4Rec: A Pure MLP Architecture for Sequential Recommendations

Muyang Li
Xiangyu Zhao
Chuan Lyu
Minghao Zhao
Runze Wu
Ruocheng Guo

Self-attention models have achieved state-of-the-art performance in sequential recommender systems by capturing the sequential dependencies among user-item interactions. However, they rely on positional embeddings to retain the sequential information, which may break the semantics of item embeddings. In addition, most existing works assume that such sequential dependencies exist solely in the item embeddings, but neglect their existence among the item features. In this work, we propose a novel sequential recommender system (MLP4Rec) based on the recent advances of MLP-based architectures, which is naturally sensitive to the order of items in a sequence. To be specific, we develop a tri-directional fusion scheme to coherently capture sequential, cross-channel and cross-feature correlations. Extensive experiments demonstrate the effectiveness of MLP4Rec over various representative baselines upon two benchmark datasets. The simple architecture of MLP4Rec also leads to the linear computational complexity as well as much fewer model parameters than existing self-attention methods.

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AAAI Conference 2022 Conference Paper

Multi-Dimensional Prediction of Guild Health in Online Games: A Stability-Aware Multi-Task Learning Approach

Chuang Zhao
Hongke Zhao
Runze Wu
Qilin Deng
Yu Ding
Jianrong Tao
Changjie Fan

Guild is the most important long-term virtual community and emotional bond in massively multiplayer online roleplaying games (MMORPGs). It matters a lot to the player retention and game ecology how the guilds are going, e. g. , healthy or not. The main challenge now is to characterize and predict the guild health in a quantitative, dynamic, and multi-dimensional manner based on complicated multimedia data streams. To this end, we propose a novel framework, namely Stability-Aware Multi-task Learning Approach (SAMLA) to address these challenges. Specifically, different media-specific modules are designed to extract information from multiple media types of tabular data, time series characteristics, and heterogeneous graphs. To capture the dynamics of guild health, we introduce a representation encoder to provide a time-series view of multi-media data that is used for task prediction. Inspired by well-received theories on organization management, we delicately define five specific and quantitative dimensions of guild health and make parallel predictions based on a multi-task approach. Besides, we devise a novel auxiliary task, i. e. , the guild stability, to boost the performance of the guild health prediction task. Extensive experiments on a real-world large-scale MMORPG dataset verify that our proposed method outperforms the state-of-the-art methods in the task of organizational health characterization and prediction. Moreover, our work has been practically deployed in online MMORPG, and case studies clearly illustrate the significant value.

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AAAI Conference 2021 Conference Paper

NeuralAC: Learning Cooperation and Competition Effects for Match Outcome Prediction

Yin Gu
Qi Liu
Kai Zhang
Zhenya Huang
Runze Wu
Jianrong Tao

Match outcome prediction in group comparison setting is a challenging but important task. Existing works mainly focus on learning individual effects or mining limited interactions between teammates, which is not sufficient for capturing complex interactions between teammates as well as between opponents. Besides, the importance of interacting with different characters is still largely underexplored. To this end, we propose a novel Neural Attentional Cooperation-competition model (NeuralAC), which incorporates weighted-cooperation effects (i. e. , intra-team interactions) and weighted-competition effects (i. e. , inter-team interactions) for predicting match outcomes. Specifically, we first project individuals to latent vectors and learn complex interactions through deep neural networks. Then, we design two novel attention-based mechanisms to capture the importance of intra-team and inter-team interactions, which enhance NeuralAC with both accuracy and interpretability. Furthermore, we demonstrate NeuralAC can generalize several previous works. To evaluate the performances of NeuralAC, we conduct extensive experiments on four E-sports datasets. The experimental results clearly verify the effectiveness of NeuralAC compared with several state-of-the-art methods.

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AAAI Conference 2021 Conference Paper

Reinforcement Learning with a Disentangled Universal Value Function for Item Recommendation

Kai Wang
Zhene Zou
Qilin Deng
Jianrong Tao
Runze Wu
Changjie Fan
Liang Chen
Peng Cui

In recent years, there are great interests as well as challenges in applying reinforcement learning (RL) to recommendation systems (RS). In this paper, we summarize three key practical challenges of large-scale RL-based recommender systems: massive state and action spaces, high-variance environment, and the unspecific reward setting in recommendation. All these problems remain largely unexplored in the existing literature and make the application of RL challenging. We develop a model-based reinforcement learning framework, called GoalRec. Inspired by the ideas of world model (model-based), value function estimation (model-free), and goal-based RL, a novel disentangled universal value function designed for item recommendation is proposed. It can generalize to various goals that the recommender may have, and disentangle the stochastic environmental dynamics and high-variance reward signals accordingly. As a part of the value function, free from the sparse and high-variance reward signals, a high-capacity reward-independent world model is trained to simulate complex environmental dynamics under a certain goal. Based on the predicted environmental dynamics, the disentangled universal value function is related to the user’s future trajectory instead of a monolithic state and a scalar reward. We demonstrate the superiority of GoalRec over previous approaches in terms of the above three practical challenges in a series of simulations and a real application.

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AAAI Conference 2018 Conference Paper

Confidence-Aware Matrix Factorization for Recommender Systems

Chao Wang
Qi Liu
Runze Wu
Enhong Chen
Chuanren Liu
Xunpeng Huang
Zhenya Huang

Collaborative ﬁltering (CF), particularly matrix factorization (MF) based methods, have been widely used in recommender systems. The literature has reported that matrix factorization methods often produce superior accuracy of rating prediction in recommender systems. However, existing matrix factorization methods rarely consider conﬁdence of the rating prediction and thus cannot support advanced recommendation tasks. In this paper, we propose a Conﬁdence-aware Matrix Factorization (CMF) framework to simultaneously optimize the accuracy of rating prediction and measure the prediction conﬁdence in the model. Speciﬁcally, we introduce variance parameters for both users and items in the matrix factorization process. Then, prediction interval can be computed to measure conﬁdence for each predicted rating. These conﬁdence quantities can be used to enhance the quality of recommendation results based on Conﬁdence-aware Ranking (CR). We also develop two effective implementations of our framework to compute the conﬁdence-aware matrix factorization for large-scale data. Finally, extensive experiments on three real-world datasets demonstrate the effectiveness of our framework from multiple perspectives.

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TIST Journal 2018 Journal Article

Fuzzy Cognitive Diagnosis for Modelling Examinee Performance

Qi Liu
Runze Wu
Enhong Chen
Guandong Xu
Yu Su
Zhigang Chen
Guoping Hu

Recent decades have witnessed the rapid growth of educational data mining (EDM), which aims at automatically extracting valuable information from large repositories of data generated by or related to people’s learning activities in educational settings. One of the key EDM tasks is cognitive modelling with examination data, and cognitive modelling tries to profile examinees by discovering their latent knowledge state and cognitive level (e.g. the proficiency of specific skills). However, to the best of our knowledge, the problem of extracting information from both objective and subjective examination problems to achieve more precise and interpretable cognitive analysis remains underexplored. To this end, we propose a fuzzy cognitive diagnosis framework (FuzzyCDF) for examinees’ cognitive modelling with both objective and subjective problems. Specifically, to handle the partially correct responses on subjective problems, we first fuzzify the skill proficiency of examinees. Then we combine fuzzy set theory and educational hypotheses to model the examinees’ mastery on the problems based on their skill proficiency. Finally, we simulate the generation of examination score on each problem by considering slip and guess factors. In this way, the whole diagnosis framework is built. For further comprehensive verification, we apply our FuzzyCDF to three classical cognitive assessment tasks, i.e., predicting examinee performance, slip and guess detection, and cognitive diagnosis visualization. Extensive experiments on three real-world datasets for these assessment tasks prove that FuzzyCDF can reveal the knowledge states and cognitive level of the examinees effectively and interpretatively.

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IJCAI Conference 2015 Conference Paper

Cognitive Modelling for Predicting Examinee Performance

Runze Wu
Qi Liu
Yuping Liu
Enhong Chen
Yu Su
Zhigang Chen
Guoping Hu

Cognitive modelling can discover the latent characteristics of examinees for predicting their performance (i. e. scores) on each problem. As cognitive modelling is important for numerous applications, e. g. personalized remedy recommendation, some solutions have been designed in the literature. However, the problem of extracting information from both objective and subjective problems to get more precise and interpretable cognitive analysis is still underexplored. To this end, we propose a fuzzy cognitive diagnosis framework (FuzzyCDF) for examinees’ cognitive modelling with both objective and subjective problems. Specifically, to handle the partially correct responses on subjective problems, we first fuzzify the skill proficiency of examinees. Then, we combine fuzzy set theory and educational hypotheses to model the examinees’ mastery on the problems. Further, we simulate the generation of examination scores by considering both slip and guess factors. Extensive experiments on three realworld datasets prove that FuzzyCDF can predict examinee performance more effectively, and the output of FuzzyCDF is also interpretative.

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