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Titlebook: Machine Learning and Knowledge Discovery in Databases; European Conference, Massih-Reza Amini,Stéphane Canu,Grigorios Tsoumaka Conference p

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发表于 2025-3-21 16:58:19 | 显示全部楼层 |阅读模式
书目名称Machine Learning and Knowledge Discovery in Databases
副标题European Conference,
编辑Massih-Reza Amini,Stéphane Canu,Grigorios Tsoumaka
视频video
丛书名称Lecture Notes in Computer Science
图书封面Titlebook: Machine Learning and Knowledge Discovery in Databases; European Conference, Massih-Reza Amini,Stéphane Canu,Grigorios Tsoumaka Conference p
描述The multi-volume set LNAI 13713 until 13718 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2022, which took place in Grenoble, France, in September 2022..The 236 full papers presented in these proceedings were carefully reviewed and selected from a total of 1060 submissions. In addition, the proceedings include 17 Demo Track contributions...The volumes are organized in topical sections as follows:..Part I:. Clustering and dimensionality reduction; anomaly detection; interpretability and explainability; ranking and recommender systems; transfer and multitask learning; ..Part II: .Networks and graphs; knowledge graphs; social network analysis; graph neural networks; natural language processing and text mining; conversational systems; ..Part III: .Deep learning; robust and adversarial machine learning; generative models; computer vision; meta-learning, neural architecture search; ..Part IV:. Reinforcement learning; multi-agent reinforcement learning; bandits and online learning; active and semi-supervised learning; private and federated learning;..Part V:. Supervised learning; probabilistic inference;
出版日期Conference proceedings 2023
关键词artificial intelligence; autonomous agents; computer networks; computer security; computer systems; corre
版次1
doihttps://doi.org/10.1007/978-3-031-26412-2
isbn_softcover978-3-031-26411-5
isbn_ebook978-3-031-26412-2Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
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Multi-Objective Actor-Critics for Real-Time Bidding in Display Advertisingering display cost, Return on Investment (ROI), and other influential Key Performance Indicators (KPIs), large ad platforms try to balance the trade-off among various goals in dynamics. To address the challenge, we propose a .ulti-.bjec.ve .ctor-.ritics algorithm based on reinforcement learning (RL)
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Oracle-SAGE: Planning Ahead in Graph-Based Deep Reinforcement Learninginput. Where available (such as some robotic control domains), low dimensional vector inputs outperform their image based counterparts, but it is challenging to represent complex dynamic environments in this manner. Relational reinforcement learning instead represents the world as a set of objects a
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State Representation Learning for Goal-Conditioned Reinforcement Learningdding space where distances between pairs of embedded states correspond to the minimum number of actions needed to transition between them. Compared to previous methods, our approach does not require any domain knowledge, learning from offline and unlabeled data. We show how this representation can
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Imitation Learning with Sinkhorn Distances experts and learners is crucial in their effectiveness in learning from demonstrations. In this paper, we present tractable solutions by formulating imitation learning as minimization of the Sinkhorn distance between occupancy measures. The formulation combines the valuable properties of optimal tr
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Safe Exploration Method for Reinforcement Learning Under Existence of Disturbance property, we have to take the risk into consideration when we apply those algorithms to safety-critical problems especially in real environments. In this study, we deal with a safe exploration problem in reinforcement learning under the existence of disturbance. We define the safety during learning
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