IJCAI : proceedings of the conference最新文献

Two-stage Risk Control with Application to Ranked Retrieval. 两阶段风险控制及其在排序检索中的应用。

IJCAI : proceedings of the conference Pub Date : 2025-08-01 DOI: 10.24963/ijcai.2025/1012

Yunpeng Xu, Mufang Ying, Wenge Guo, Zhi Wei

引用次数: 0

Predictive Modeling with Temporal Graphical Representation on Electronic Health Records. 利用电子健康记录的时态图形表示进行预测建模。

IJCAI : proceedings of the conference Pub Date : 2024-08-01 DOI: 10.24963/ijcai.2024/637

Jiayuan Chen, Changchang Yin, Yuanlong Wang, Ping Zhang

{"title":"Predictive Modeling with Temporal Graphical Representation on Electronic Health Records.","authors":"Jiayuan Chen, Changchang Yin, Yuanlong Wang, Ping Zhang","doi":"10.24963/ijcai.2024/637","DOIUrl":"10.24963/ijcai.2024/637","url":null,"abstract":"Deep learning-based predictive models, leveraging Electronic Health Records (EHR), are receiving increasing attention in healthcare. An effective representation of a patient's EHR should hierarchically encompass both the temporal relationships between historical visits and medical events, and the inherent structural information within these elements. Existing patient representation methods can be roughly categorized into sequential representation and graphical representation. The sequential representation methods focus only on the temporal relationships among longitudinal visits. On the other hand, the graphical representation approaches, while adept at extracting the graph-structured relationships between various medical events, fall short in effectively integrate temporal information. To capture both types of information, we model a patient's EHR as a novel temporal heterogeneous graph. This graph includes historical visits nodes and medical events nodes. It propagates structured information from medical event nodes to visit nodes and utilizes time-aware visit nodes to capture changes in the patient's health status. Furthermore, we introduce a novel temporal graph transformer (TRANS) that integrates temporal edge features, global positional encoding, and local structural encoding into heterogeneous graph convolution, capturing both temporal and structural information. We validate the effectiveness of TRANS through extensive experiments on three real-world datasets. The results show that our proposed approach achieves state-of-the-art performance.","PeriodicalId":73334,"journal":{"name":"IJCAI : proceedings of the conference","volume":"2024 ","pages":"5763-5771"},"PeriodicalIF":0.0,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11446542/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142367720","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Visual Attention Prompted Prediction and Learning. 视觉注意促进预测和学习。

IJCAI : proceedings of the conference Pub Date : 2024-08-01 DOI: 10.24963/ijcai.2024/610

Yifei Zhang, Bo Pan, Siyi Gu, Guangji Bai, Meikang Qiu, Xiaofeng Yang, Liang Zhao

{"title":"Visual Attention Prompted Prediction and Learning.","authors":"Yifei Zhang, Bo Pan, Siyi Gu, Guangji Bai, Meikang Qiu, Xiaofeng Yang, Liang Zhao","doi":"10.24963/ijcai.2024/610","DOIUrl":"10.24963/ijcai.2024/610","url":null,"abstract":"Visual explanation (attention)-guided learning uses not only labels but also explanations to guide the model reasoning process. While visual attention-guided learning has shown promising results, it requires a large number of explanation annotations that are time-consuming to prepare. However, in many real-world situations, it is usually desired to prompt the model with visual attention without model retraining. For example, when doing AI-assisted cancer classification on a medical image, users (e.g., clinicians) can provide the AI model with visual attention prompts on which areas are indispensable and which are precluded. Despite its promising objectives, achieving visual attention-prompted prediction presents several major challenges: 1) How can the visual prompt be effectively integrated into the model's reasoning process? 2) How should the model handle samples that lack visual prompts? 3) What is the impact on the model's performance when a visual prompt is imperfect? This paper introduces a novel framework for visual attention prompted prediction and learning, utilizing visual prompts to steer the model's reasoning process. To improve performance in non-prompted situations and align it with prompted scenarios, we propose a co-training approach for both non-prompted and prompted models, ensuring they share similar parameters and activation. Additionally, for instances where the visual prompt does not encompass the entire input image, we have developed innovative attention prompt refinement methods. These methods interpolate the incomplete prompts while maintaining alignment with the model's explanations. Extensive experiments on four datasets demonstrate the effectiveness of our proposed framework in enhancing predictions for samples both with and without prompt.","PeriodicalId":73334,"journal":{"name":"IJCAI : proceedings of the conference","volume":"2024 ","pages":"5517-5525"},"PeriodicalIF":0.0,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13499441/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148802469","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Recent Advances in Predictive Modeling with Electronic Health Records. 电子健康记录预测建模的最新进展。

IJCAI : proceedings of the conference Pub Date : 2024-08-01 DOI: 10.24963/ijcai.2024/914

Jiaqi Wang, Junyu Luo, Muchao Ye, Xiaochen Wang, Yuan Zhong, Aofei Chang, Guanjie Huang, Ziyi Yin, Cao Xiao, Jimeng Sun, Fenglong Ma

引用次数: 0

ReBandit: Random Effects Based Online RL Algorithm for Reducing Cannabis Use. ReBandit：基于随机效应的在线RL算法减少大麻使用。

IJCAI : proceedings of the conference Pub Date : 2024-08-01

Susobhan Ghosh, Yongyi Guo, Pei-Yao Hung, Lara Coughlin, Erin Bonar, Inbal Nahum-Shani, Maureen Walton, Susan Murphy

{"title":"ReBandit: Random Effects Based Online RL Algorithm for Reducing Cannabis Use.","authors":"Susobhan Ghosh, Yongyi Guo, Pei-Yao Hung, Lara Coughlin, Erin Bonar, Inbal Nahum-Shani, Maureen Walton, Susan Murphy","doi":"","DOIUrl":"","url":null,"abstract":"The escalating prevalence of cannabis use, and associated cannabis-use disorder (CUD), poses a significant public health challenge globally. With a notably wide treatment gap, especially among emerging adults (EAs; ages 18-25), addressing cannabis use and CUD remains a pivotal objective within the 2030 United Nations Agenda for Sustainable Development Goals (SDG). In this work, we develop an online reinforcement learning (RL) algorithm called reBandit which will be utilized in a mobile health study to deliver personalized mobile health interventions aimed at reducing cannabis use among EAs. reBandit utilizes random effects and informative Bayesian priors to learn quickly and efficiently in noisy mobile health environments. Moreover, reBandit employs Empirical Bayes and optimization techniques to autonomously update its hyper-parameters online. To evaluate the performance of our algorithm, we construct a simulation testbed using data from a prior study, and compare against commonly used algorithms in mobile health studies. We show that reBandit performs equally well or better than all the baseline algorithms, and the performance gap widens as population heterogeneity increases in the simulation environment, proving its adeptness to adapt to diverse population of study participants.","PeriodicalId":73334,"journal":{"name":"IJCAI : proceedings of the conference","volume":"2024 ","pages":"7278-7286"},"PeriodicalIF":0.0,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11671148/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142904166","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Adapt to Adaptation: Learning Personalization for Cross-Silo Federated Learning. 适应适应:跨筒仓联邦学习的学习个性化。

IJCAI : proceedings of the conference Pub Date : 2022-07-01 DOI: 10.24963/ijcai.2022/301

Jun Luo, Shandong Wu

引用次数: 13

Stabilizing and Enhancing Link Prediction through Deepened Graph Auto-Encoders. 通过深化图自动编码器稳定并增强链接预测。

IJCAI : proceedings of the conference Pub Date : 2022-07-01 DOI: 10.24963/ijcai.2022/498

Xinxing Wu, Qiang Cheng

引用次数: 0

RCA: A Deep Collaborative Autoencoder Approach for Anomaly Detection RCA:一种深度协同自编码器异常检测方法

IJCAI : proceedings of the conference Pub Date : 2021-08-01 DOI: 10.24963/ijcai.2021/208

Boyang Liu, Ding Wang, Kaixiang Lin, P. Tan, Jiayu Zhou

引用次数: 11

Human Gaze Assisted Artificial Intelligence: A Review. 人类凝视辅助人工智能：综述。

IJCAI : proceedings of the conference Pub Date : 2020-07-01 DOI: 10.24963/ijcai.2020/689

Ruohan Zhang, Akanksha Saran, Bo Liu, Yifeng Zhu, Sihang Guo, Scott Niekum, Dana Ballard, Mary Hayhoe

引用次数: 43

Improving Attention Mechanism in Graph Neural Networks via Cardinality Preservation. 基于基数保留的图神经网络注意机制改进。

IJCAI : proceedings of the conference Pub Date : 2020-07-01 DOI: 10.24963/ijcai.2020/194

Shuo Zhang, Lei Xie

{"title":"Improving Attention Mechanism in Graph Neural Networks via Cardinality Preservation.","authors":"Shuo Zhang, Lei Xie","doi":"10.24963/ijcai.2020/194","DOIUrl":"https://doi.org/10.24963/ijcai.2020/194","url":null,"abstract":"Graph Neural Networks (GNNs) are powerful for the representation learning of graph-structured data. Most of the GNNs use a message-passing scheme, where the embedding of a node is iteratively updated by aggregating the information from its neighbors. To achieve a better expressive capability of node influences, attention mechanism has grown to be popular to assign trainable weights to the nodes in aggregation. Though the attention-based GNNs have achieved remarkable results in various tasks, a clear understanding of their discriminative capacities is missing. In this work, we present a theoretical analysis of the representational properties of the GNN that adopts the attention mechanism as an aggregator. Our analysis determines all cases when those attention-based GNNs can always fail to distinguish certain distinct structures. Those cases appear due to the ignorance of cardinality information in attention-based aggregation. To improve the performance of attention-based GNNs, we propose cardinality preserved attention (CPA) models that can be applied to any kind of attention mechanisms. Our experiments on node and graph classification confirm our theoretical analysis and show the competitive performance of our CPA models. The code is available online: https://github.com/zetayue/CPA.","PeriodicalId":73334,"journal":{"name":"IJCAI : proceedings of the conference","volume":"2020 ","pages":"1395-1402"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7416665/pdf/nihms-1615275.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38262582","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 33