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

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

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

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 : 2019-01-01 DOI: 10.24963/ijcai.2019/557

Zhe Xu, Ufuk Topcu

{"title":"Transfer of Temporal Logic Formulas in Reinforcement Learning.","authors":"Zhe Xu, Ufuk Topcu","doi":"10.24963/ijcai.2019/557","DOIUrl":"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":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6800702/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74383366","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

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