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Baseline Regularization for Computational Drug Repositioning with Longitudinal Observational Data. 纵向观测数据计算药物重新定位的基线正则化。
IJCAI : proceedings of the conference Pub Date : 2016-07-01
Zhaobin Kuang, James Thomson, Michael Caldwell, Peggy Peissig, Ron Stewart, David Page
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引用次数: 0
Hidden Parameter Markov Decision Processes: A Semiparametric Regression Approach for Discovering Latent Task Parametrizations. 隐参数马尔可夫决策过程:一种发现潜在任务参数化的半参数回归方法。
IJCAI : proceedings of the conference Pub Date : 2016-07-01
Finale Doshi-Velez, George Konidaris
{"title":"Hidden Parameter Markov Decision Processes: A Semiparametric Regression Approach for Discovering Latent Task Parametrizations.","authors":"Finale Doshi-Velez,&nbsp;George Konidaris","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Control applications often feature tasks with similar, but not identical, dynamics. We introduce the Hidden Parameter Markov Decision Process (HiP-MDP), a framework that parametrizes a family of related dynamical systems with a low-dimensional set of latent factors, and introduce a semiparametric regression approach for learning its structure from data. We show that a learned HiP-MDP rapidly identifies the dynamics of new task instances in several settings, flexibly adapting to task variation.</p>","PeriodicalId":73334,"journal":{"name":"IJCAI : proceedings of the conference","volume":"2016 ","pages":"1432-1440"},"PeriodicalIF":0.0,"publicationDate":"2016-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5466173/pdf/nihms859880.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"35079547","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
An Efficient Framework for Constructing Generalized Locally-Induced Text Metrics 构造广义局部诱导文本度量的有效框架
IJCAI : proceedings of the conference Pub Date : 2011-07-16 DOI: 10.5591/978-1-57735-516-8/IJCAI11-198
S. Amizadeh, Shuguang Wang, M. Hauskrecht
{"title":"An Efficient Framework for Constructing Generalized Locally-Induced Text Metrics","authors":"S. Amizadeh, Shuguang Wang, M. Hauskrecht","doi":"10.5591/978-1-57735-516-8/IJCAI11-198","DOIUrl":"https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-198","url":null,"abstract":"In this paper, we propose a new framework for constructing text metrics which can be used to compare and support inferences among terms and sets of terms. Our metric is derived from data-driven kernels on graphs that let us capture global relations among terms and sets of terms, regardless of their complexity and size. To compute the metric efficiently for any two subsets of terms, we develop an approximation technique that relies on the precompiled term-term similarities. To scale-up the approach to problems with huge number of terms, we develop and experiment with a solution that sub-samples the term space. We demonstrate the benefits of the whole framework on two text inference tasks: prediction of terms in the article from its abstract and query expansion in information retrieval.","PeriodicalId":73334,"journal":{"name":"IJCAI : proceedings of the conference","volume":"59 1","pages":"1159-1164"},"PeriodicalIF":0.0,"publicationDate":"2011-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79090536","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: 5
An Efficient Framework for Constructing Generalized Locally-Induced Text Metrics. 构造广义局部诱导文本度量的有效框架。
IJCAI : proceedings of the conference Pub Date : 2011-01-01
Saeed Amizadeh, Shuguang Wang, Milos Hauskrecht
{"title":"An Efficient Framework for Constructing Generalized Locally-Induced Text Metrics.","authors":"Saeed Amizadeh,&nbsp;Shuguang Wang,&nbsp;Milos Hauskrecht","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>In this paper, we propose a new framework for constructing text metrics which can be used to compare and support inferences among terms and sets of terms. Our metric is derived from data-driven kernels on graphs that let us capture global relations among terms and sets of terms, regardless of their complexity and size. To compute the metric efficiently for any two subsets of terms, we develop an approximation technique that relies on the precompiled term-term similarities. To scale-up the approach to problems with huge number of terms, we develop and experiment with a solution that sub-samples the term space. We demonstrate the benefits of the whole framework on two text inference tasks: prediction of terms in the article from its abstract and query expansion in information retrieval.</p>","PeriodicalId":73334,"journal":{"name":"IJCAI : proceedings of the conference","volume":" ","pages":"1159-1164"},"PeriodicalIF":0.0,"publicationDate":"2011-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3264061/pdf/nihms348372.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"30414049","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
Drosophila Gene Expression Pattern Annotation through Multi-Instance Multi-Label Learning. 基于多实例多标签学习的果蝇基因表达模式标注。
IJCAI : proceedings of the conference Pub Date : 2009-01-01
Ying-Xin Li, Shuiwang Ji, Sudhir Kumar, Jieping Ye, Zhi-Hua Zhou
{"title":"Drosophila Gene Expression Pattern Annotation through Multi-Instance Multi-Label Learning.","authors":"Ying-Xin Li,&nbsp;Shuiwang Ji,&nbsp;Sudhir Kumar,&nbsp;Jieping Ye,&nbsp;Zhi-Hua Zhou","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>The Berkeley Drosophila Genome Project (BDGP) has produced a large number of gene expression patterns, many of which have been annotated textually with anatomical and developmental terms. These terms spatially correspond to local regions of the images; however, they are attached collectively to groups of images, such that it is unknown which term is assigned to which region of which image in the group. This poses a challenge to the development of the computational method to automate the textual description of expression patterns contained in each image. In this paper, we show that the underlying nature of this task matches well with a new machine learning framework, Multi-Instance Multi-Label learning (MIML). We propose a new MIML support vector machine to solve the problems that beset the annotation task. Empirical study shows that the proposed method outperforms the state-of-the-art Drosophila gene expression pattern annotation methods.</p>","PeriodicalId":73334,"journal":{"name":"IJCAI : proceedings of the conference","volume":"2009 ","pages":"1445-1450"},"PeriodicalIF":0.0,"publicationDate":"2009-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2932460/pdf/nihms110406.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"29294296","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
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