IJCAI : proceedings of the conference最新文献_第2页

Learning Disentangled Semantic Representation for Domain Adaptation 面向领域自适应的解纠缠语义表示学习

IJCAI : proceedings of the conference Pub Date : 2019-08-01 DOI: 10.24963/ijcai.2019/285

Ruichu Cai, Zijian Li, Pengfei Wei, Jie Qiao, Kun Zhang, Z. Hao

引用次数: 86

Learning Disentangled Semantic Representation for Domain Adaptation. 学习用于领域适应的分离语义表征

IJCAI : proceedings of the conference Pub Date : 2019-08-01

Ruichu Cai, Zijian Li, Pengfei Wei, Jie Qiao, Kun Zhang, Zhifeng Hao

引用次数: 0

Exploring Computational User Models for Agent Policy Summarization. 探索用于Agent策略总结的计算用户模型。

IJCAI : proceedings of the conference Pub Date : 2019-08-01

Isaac Lage, Daphna Lifschitz, Finale Doshi-Velez, Ofra Amir

{"title":"Exploring Computational User Models for Agent Policy Summarization.","authors":"Isaac Lage, Daphna Lifschitz, Finale Doshi-Velez, Ofra Amir","doi":"","DOIUrl":"","url":null,"abstract":"AI agents support high stakes decision-making processes from driving cars to prescribing drugs, making it increasingly important for human users to understand their behavior. Policy summarization methods aim to convey strengths and weaknesses of such agents by demonstrating their behavior in a subset of informative states. Some policy summarization methods extract a summary that optimizes the ability to reconstruct the agent's policy under the assumption that users will deploy inverse reinforcement learning. In this paper, we explore the use of different models for extracting summaries. We introduce an imitation learning-based approach to policy summarization; we demonstrate through computational simulations that a mismatch between the model used to extract a summary and the model used to reconstruct the policy results in worse reconstruction quality; and we demonstrate through a human-subject study that people use different models to reconstruct policies in different contexts, and that matching the summary extraction model to these can improve performance. Together, our results suggest that it is important to carefully consider user models in policy summarization.","PeriodicalId":73334,"journal":{"name":"IJCAI : proceedings of the conference","volume":"28 ","pages":"1401-1407"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7901848/pdf/nihms-1067306.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"25402383","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

Medical Concept Representation Learning from Multi-source Data. 从多源数据中学习医学概念表征

IJCAI : proceedings of the conference Pub Date : 2019-07-01 DOI: 10.24963/ijcai.2019/680

Tian Bai, Brian L Egleston, Richard Bleicher, Slobodan Vucetic

{"title":"Medical Concept Representation Learning from Multi-source Data.","authors":"Tian Bai, Brian L Egleston, Richard Bleicher, Slobodan Vucetic","doi":"10.24963/ijcai.2019/680","DOIUrl":"10.24963/ijcai.2019/680","url":null,"abstract":"Representing words as low dimensional vectors is very useful in many natural language processing tasks. This idea has been extended to medical domain where medical codes listed in medical claims are represented as vectors to facilitate exploratory analysis and predictive modeling. However, depending on a type of a medical provider, medical claims can use medical codes from different ontologies or from a combination of ontologies, which complicates learning of the representations. To be able to properly utilize such multi-source medical claim data, we propose an approach that represents medical codes from different ontologies in the same vector space. We first modify the Pointwise Mutual Information (PMI) measure of similarity between the codes. We then develop a new negative sampling method for word2vec model that implicitly factorizes the modified PMI matrix. The new approach was evaluated on the code cross-reference problem, which aims at identifying similar codes across different ontologies. In our experiments, we evaluated cross-referencing between ICD-9 and CPT medical code ontologies. Our results indicate that vector representations of codes learned by the proposed approach provide superior cross-referencing when compared to several existing approaches.","PeriodicalId":73334,"journal":{"name":"IJCAI : proceedings of the conference","volume":"2019 ","pages":"4897-4903"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7047512/pdf/nihms-1558151.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"37693039","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

Exploring Computational User Models for Agent Policy Summarization 探索用于Agent策略总结的计算用户模型

IJCAI : proceedings of the conference Pub Date : 2019-05-01 DOI: 10.24963/ijcai.2019/194

Isaac Lage, Daphna Lifschitz, F. Doshi-Velez, Ofra Amir

{"title":"Exploring Computational User Models for Agent Policy Summarization","authors":"Isaac Lage, Daphna Lifschitz, F. Doshi-Velez, Ofra Amir","doi":"10.24963/ijcai.2019/194","DOIUrl":"https://doi.org/10.24963/ijcai.2019/194","url":null,"abstract":"AI agents support high stakes decision-making processes from driving cars to prescribing drugs, making it increasingly important for human users to understand their behavior. Policy summarization methods aim to convey strengths and weaknesses of such agents by demonstrating their behavior in a subset of informative states. Some policy summarization methods extract a summary that optimizes the ability to reconstruct the agent's policy under the assumption that users will deploy inverse reinforcement learning. In this paper, we explore the use of different models for extracting summaries. We introduce an imitation learning-based approach to policy summarization; we demonstrate through computational simulations that a mismatch between the model used to extract a summary and the model used to reconstruct the policy results in worse reconstruction quality; and we demonstrate through a human-subject study that people use different models to reconstruct policies in different contexts, and that matching the summary extraction model to these can improve performance. Together, our results suggest that it is important to carefully consider user models in policy summarization.","PeriodicalId":73334,"journal":{"name":"IJCAI : proceedings of the conference","volume":"31 1","pages":"1401-1407"},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82365179","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: 55

Hierarchical Active Learning with Group Proportion Feedback. 基于群体比例反馈的分层主动学习。

IJCAI : proceedings of the conference Pub Date : 2018-07-01 DOI: 10.24963/ijcai.2018/351

Zhipeng Luo, Milos Hauskrecht

引用次数: 8

Relatedness-based Multi-Entity Summarization. 基于关联的多实体摘要。

IJCAI : proceedings of the conference Pub Date : 2017-08-01 DOI: 10.24963/ijcai.2017/147

Kalpa Gunaratna, Amir Hossein Yazdavar, Krishnaprasad Thirunarayan, Amit Sheth, Gong Cheng

{"title":"Relatedness-based Multi-Entity Summarization.","authors":"Kalpa Gunaratna, Amir Hossein Yazdavar, Krishnaprasad Thirunarayan, Amit Sheth, Gong Cheng","doi":"10.24963/ijcai.2017/147","DOIUrl":"https://doi.org/10.24963/ijcai.2017/147","url":null,"abstract":"Representing world knowledge in a machine processable format is important as entities and their descriptions have fueled tremendous growth in knowledge-rich information processing platforms, services, and systems. Prominent applications of knowledge graphs include search engines (e.g., Google Search and Microsoft Bing), email clients (e.g., Gmail), and intelligent personal assistants (e.g., Google Now, Amazon Echo, and Apple's Siri). In this paper, we present an approach that can summarize facts about a collection of entities by analyzing their relatedness in preference to summarizing each entity in isolation. Specifically, we generate informative entity summaries by selecting: (i) inter-entity facts that are similar and (ii) intra-entity facts that are important and diverse. We employ a constrained knapsack problem solving approach to efficiently compute entity summaries. We perform both qualitative and quantitative experiments and demonstrate that our approach yields promising results compared to two other stand-alone state-of-the-art entity summarization approaches.","PeriodicalId":73334,"journal":{"name":"IJCAI : proceedings of the conference","volume":"2017 ","pages":"1060-1066"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5644492/pdf/nihms911351.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"35532066","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: 15

Predicting Alzheimer's Disease Cognitive Assessment via Robust Low-Rank Structured Sparse Model. 基于鲁棒低秩结构稀疏模型的阿尔茨海默病认知评估预测。

IJCAI : proceedings of the conference Pub Date : 2017-08-01 DOI: 10.24963/ijcai.2017/542

Jie Xu, Cheng Deng, Xinbo Gao, Dinggang Shen, Heng Huang

{"title":"Predicting Alzheimer's Disease Cognitive Assessment via Robust Low-Rank Structured Sparse Model.","authors":"Jie Xu, Cheng Deng, Xinbo Gao, Dinggang Shen, Heng Huang","doi":"10.24963/ijcai.2017/542","DOIUrl":"https://doi.org/10.24963/ijcai.2017/542","url":null,"abstract":"Alzheimer's disease (AD) is a neurodegenerative disorder with slow onset, which could result in the deterioration of the duration of persistent neurological dysfunction. How to identify the informative longitudinal phenotypic neuroimaging markers and predict cognitive measures are crucial to recognize AD at early stage. Many existing models related imaging measures to cognitive status using regression models, but they did not take full consideration of the interaction between cognitive scores. In this paper, we propose a robust low-rank structured sparse regression method (RLSR) to address this issue. The proposed model simultaneously selects effective features and learns the underlying structure between cognitive scores by utilizing novel mixed structured sparsity inducing norms and low-rank approximation. In addition, an efficient algorithm is derived to solve the proposed non-smooth objective function with proved convergence. Empirical studies on cognitive data of the ADNI cohort demonstrate the superior performance of the proposed method.","PeriodicalId":73334,"journal":{"name":"IJCAI : proceedings of the conference","volume":"2017 ","pages":"3880-3886"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.24963/ijcai.2017/542","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"36031641","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: 17

Causal Discovery from Nonstationary/Heterogeneous Data: Skeleton Estimation and Orientation Determination. 从非稳态/异构数据中发现因果关系：骨架估计与方向确定

IJCAI : proceedings of the conference Pub Date : 2017-08-01 DOI: 10.24963/ijcai.2017/187

Kun Zhang, Biwei Huang, Jiji Zhang, Clark Glymour, Bernhard Schölkopf

{"title":"Causal Discovery from Nonstationary/Heterogeneous Data: Skeleton Estimation and Orientation Determination.","authors":"Kun Zhang, Biwei Huang, Jiji Zhang, Clark Glymour, Bernhard Schölkopf","doi":"10.24963/ijcai.2017/187","DOIUrl":"10.24963/ijcai.2017/187","url":null,"abstract":"It is commonplace to encounter nonstationary or heterogeneous data, of which the underlying generating process changes over time or across data sets (the data sets may have different experimental conditions or data collection conditions). Such a distribution shift feature presents both challenges and opportunities for causal discovery. In this paper we develop a principled framework for causal discovery from such data, called Constraint-based causal Discovery from Nonstationary/heterogeneous Data (CD-NOD), which addresses two important questions. First, we propose an enhanced constraint-based procedure to detect variables whose local mechanisms change and recover the skeleton of the causal structure over observed variables. Second, we present a way to determine causal orientations by making use of independence changes in the data distribution implied by the underlying causal model, benefiting from information carried by changing distributions. Experimental results on various synthetic and real-world data sets are presented to demonstrate the efficacy of our methods.","PeriodicalId":73334,"journal":{"name":"IJCAI : proceedings of the conference","volume":"2017 ","pages":"1347-1353"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5617646/pdf/nihms904880.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"35461829","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

Constructing Abstraction Hierarchies Using a Skill-Symbol Loop. 使用技能-符号循环构造抽象层次结构。

IJCAI : proceedings of the conference Pub Date : 2016-07-01

George Konidaris

引用次数: 0