IJCAI : proceedings of the conference最新文献

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

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

Mixed-Variable Bayesian Optimization 混合变量贝叶斯优化

IJCAI : proceedings of the conference Pub Date : 2020-01-01 DOI: 10.24963/ijcai.2020/365

Erik A. Daxberger, Anastasia Makarova, M. Turchetta, Andreas Krause

{"title":"Mixed-Variable Bayesian Optimization","authors":"Erik A. Daxberger, Anastasia Makarova, M. Turchetta, Andreas Krause","doi":"10.24963/ijcai.2020/365","DOIUrl":"https://doi.org/10.24963/ijcai.2020/365","url":null,"abstract":"The optimization of expensive to evaluate, black-box, mixed-variable functions, i.e. functions that have continuous and discrete inputs, is a difficult and yet pervasive problem in science and engineering. In Bayesian optimization (BO), special cases of this problem that consider fully continuous or fully discrete domains have been widely studied. However, few methods exist for mixed-variable domains and none of them can handle discrete constraints that arise in many real-world applications. In this paper, we introduce MiVaBo, a novel BO algorithm for the efficient optimization of mixed-variable functions combining a linear surrogate model based on expressive feature representations with Thompson sampling. We propose an effective method to optimize its acquisition function, a challenging problem for mixed-variable domains, making MiVaBo the first BO method that can handle complex constraints over the discrete variables. Moreover, we provide the first convergence analysis of a mixed-variable BO algorithm. Finally, we show that MiVaBo is significantly more sample efficient than state-of-the-art mixed-variable BO algorithms on several hyperparameter tuning tasks, including the tuning of deep generative models.","PeriodicalId":73334,"journal":{"name":"IJCAI : proceedings of the conference","volume":"25 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86161869","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 35

What Does the Evidence Say? Models to Help Make Sense of the Biomedical Literature. 证据怎么说?帮助理解生物医学文献的模型。

IJCAI : proceedings of the conference Pub Date : 2019-08-01 DOI: 10.24963/ijcai.2019/899

Byron C Wallace

{"title":"What Does the Evidence Say? Models to Help Make Sense of the Biomedical Literature.","authors":"Byron C Wallace","doi":"10.24963/ijcai.2019/899","DOIUrl":"https://doi.org/10.24963/ijcai.2019/899","url":null,"abstract":"Ideally decisions regarding medical treatments would be informed by the totality of the available evidence. The best evidence we currently have is in published natural language articles describing the conduct and results of clinical trials. Because these are unstructured, it is difficult for domain experts (e.g., physicians) to sort through and appraise the evidence pertaining to a given clinical question. Natural language technologies have the potential to improve access to the evidence via semi-automated processing of the biomedical literature. In this brief paper I highlight work on developing tasks, corpora, and models to support semi-automated evidence retrieval and extraction. The aim is to design models that can consume articles describing clinical trials and automatically extract from these key clinical variables and findings, and estimate their reliability. Completely automating 'machine reading' of evidence remains a distant aim given current technologies; the more immediate hope is to use such technologies to help domain experts access and make sense of unstructured biomedical evidence more efficiently, with the ultimate aim of improving patient care. Aside from their practical importance, these tasks pose core NLP challenges that directly motivate methodological innovation.","PeriodicalId":73334,"journal":{"name":"IJCAI : proceedings of the conference","volume":"2019 ","pages":"6416-6420"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8136417/pdf/nihms-1066622.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39023271","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: 3

DDL: Deep Dictionary Learning for Predictive Phenotyping. DDL：用于预测表型的深度字典学习。

IJCAI : proceedings of the conference Pub Date : 2019-08-01 DOI: 10.24963/ijcai.2019/812

Tianfan Fu, Trong Nghia Hoang, Cao Xiao, Jimeng Sun

引用次数: 0

Transfer of Temporal Logic Formulas in Reinforcement Learning 时间逻辑公式在强化学习中的迁移

IJCAI : proceedings of the conference Pub Date : 2019-08-01 DOI: 10.24963/IJCAI.2019/557

Zhe Xu, U. Topcu

{"title":"Transfer of Temporal Logic Formulas in Reinforcement Learning","authors":"Zhe Xu, U. Topcu","doi":"10.24963/IJCAI.2019/557","DOIUrl":"https://doi.org/10.24963/IJCAI.2019/557","url":null,"abstract":"Transferring high-level knowledge from a source task to a target task is an effective way to expedite reinforcement learning (RL). For example, propositional logic and first-order logic have been used as representations of such knowledge. We study the transfer of knowledge between tasks in which the timing of the events matters. We call such tasks temporal tasks. We concretize similarity between temporal tasks through a notion of logical transferability, and develop a transfer learning approach between different yet similar temporal tasks. We first propose an inference technique to extract metric interval temporal logic (MITL) formulas in sequential disjunctive normal form from labeled trajectories collected in RL of the two tasks. If logical transferability is identified through this inference, we construct a timed automaton for each sequential conjunctive subformula of the inferred MITL formulas from both tasks. We perform RL on the extended state which includes the locations and clock valuations of the timed automata for the source task. We then establish mappings between the corresponding components (clocks, locations, etc.) of the timed automata from the two tasks, and transfer the extended Q-functions based on the established mappings. Finally, we perform RL on the extended state for the target task, starting with the transferred extended Q-functions. Our implementation results show, depending on how similar the source task and the target task are, that the sampling efficiency for the target task can be improved by up to one order of magnitude by performing RL in the extended state space, and further improved by up to another order of magnitude using the transferred extended Q-functions.","PeriodicalId":73334,"journal":{"name":"IJCAI : proceedings of the conference","volume":"19 1","pages":"4010-4018"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74383366","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 42