標(biāo)題: Titlebook: Machine Learning and Knowledge Discovery in Databases; European Conference, Massih-Reza Amini,Stéphane Canu,Grigorios Tsoumaka Conference p [打印本頁(yè)] 作者: Myelopathy 時(shí)間: 2025-3-21 16:58
書(shū)目名稱Machine Learning and Knowledge Discovery in Databases影響因子(影響力)
書(shū)目名稱Machine Learning and Knowledge Discovery in Databases影響因子(影響力)學(xué)科排名
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書(shū)目名稱Machine Learning and Knowledge Discovery in Databases被引頻次
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書(shū)目名稱Machine Learning and Knowledge Discovery in Databases讀者反饋
書(shū)目名稱Machine Learning and Knowledge Discovery in Databases讀者反饋學(xué)科排名
作者: ACTIN 時(shí)間: 2025-3-21 20:54
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)作者: Musket 時(shí)間: 2025-3-22 04:24 作者: 確定 時(shí)間: 2025-3-22 04:40
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作者: 昏暗 時(shí)間: 2025-3-22 10:57 作者: 吸引人的花招 時(shí)間: 2025-3-22 16:31
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 作者: TERRA 時(shí)間: 2025-3-22 18:49 作者: 發(fā)電機(jī) 時(shí)間: 2025-3-22 22:41
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作者: anaerobic 時(shí)間: 2025-3-23 01:47
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作者: 我沒(méi)有強(qiáng)迫 時(shí)間: 2025-3-23 08:41 作者: 明智的人 時(shí)間: 2025-3-23 12:05
Heterogeneity Breaks the?Game: Evaluating Cooperation-Competition with?Multisets of?Agentsboth. Several evaluation approaches have been introduced in some of these scenarios, from homogeneous competitive multi-agent systems, using a simple average or sophisticated ranking protocols, to completely heterogeneous cooperative scenarios, using the Shapley value. However, we lack a general eva作者: Mercurial 時(shí)間: 2025-3-23 14:13
Constrained Multiagent Reinforcement Learning for?Large Agent Populationronment, and scalability challenges. While several scalable multiagent RL (MARL) methods have been proposed, relatively few approaches exist for large scale . MARL settings. To address this, we first formulate the constrained MARL problem in a collective multiagent setting where interactions among a作者: 使服水土 時(shí)間: 2025-3-23 21:44 作者: 臭名昭著 時(shí)間: 2025-3-23 23:54
Team-Imitate-Synchronize for?Solving Dec-POMDPs model of the environment struggle with tasks that require sequences of collaborative actions, while Dec-POMDP algorithms that use such models to compute near-optimal policies, scale poorly. In this paper, we suggest the Team-Imitate-Synchronize (TIS) approach, a heuristic, model-based method for so作者: Vasoconstrictor 時(shí)間: 2025-3-24 06:21 作者: Cytokines 時(shí)間: 2025-3-24 06:47
MAVIPER: Learning Decision Tree Policies for?Interpretable Multi-agent Reinforcement Learnings to interpret and understand. On the other hand, existing work on interpretable reinforcement learning (RL) has shown promise in extracting more interpretable decision tree-based policies from neural networks, but only in the single-agent setting. To fill this gap, we propose the first set of algor作者: diathermy 時(shí)間: 2025-3-24 14:36 作者: Intuitive 時(shí)間: 2025-3-24 16:30 作者: 凝視 時(shí)間: 2025-3-24 22:04
Chengyin Li,Zheng Dong,Nathan Fisher,Dongxiao Zhu einem Grundstock von Computern und Internetanschlüssen ausgestattet, und viele Kantone haben den verst?rkten Einbezug digitaler Medien in den Unterricht auf ihre Agenda gesetzt (vgl. SFIB, 2008). Ein wichtiges Element dieser Anstrengungen war u. a. die Einrichtung der nationalen Lernplattform educa作者: 諂媚于人 時(shí)間: 2025-3-25 01:04 作者: painkillers 時(shí)間: 2025-3-25 06:50 作者: 變形 時(shí)間: 2025-3-25 08:53
Md Masudur Rahman,Yexiang Xueduelle Unterstützungen entsprechen k?nnen, stellt eine Anforderung an p?dagogische Institutionen und ihre Akteur*innen dar, die unter Stichworten wie zum Beispiel ?Integrative/inklusive Qualit?t, Bildungsstandards und Bildungsbarrieren“ (Geiling und Gille 2005, S. 103ff.) seit langem diskutiert wird作者: 變色龍 時(shí)間: 2025-3-25 13:46
Georgios Papagiannis,Yunpeng Liund Professionalisierung.Der Band diskutiert die Begleitung von Lernprozessen in Bildungsinstitutionen auf verschiedenen Ebenen und durch unterschiedliche Akteur*innen im Kontext aktueller bildungspolitischer und gesellschaftlicher Ver?nderungsprozesse. Im Fokus steht die Frage inwiefern die Begleit作者: Countermand 時(shí)間: 2025-3-25 19:42 作者: foppish 時(shí)間: 2025-3-25 20:31
Avishek Ghosh,Sayak Ray Chowdhuryduelle Unterstützungen entsprechen k?nnen, stellt eine Anforderung an p?dagogische Institutionen und ihre Akteur*innen dar, die unter Stichworten wie zum Beispiel ?Integrative/inklusive Qualit?t, Bildungsstandards und Bildungsbarrieren“ (Geiling und Gille 2005, S. 103ff.) seit langem diskutiert wird作者: diabetes 時(shí)間: 2025-3-26 02:54 作者: BARB 時(shí)間: 2025-3-26 05:49 作者: Cabinet 時(shí)間: 2025-3-26 11:29 作者: 邊緣帶來(lái)墨水 時(shí)間: 2025-3-26 13:56
zessen im Rahmen einer Fortbildung zu inklusivem Mathematikunterricht in dieser Arbeit von besonderer Bedeutung sind. Zun?chst erfolgt die Definition zentraler Begrifflichkeiten, dabei wird vor allem das Verst?ndnis der Begriffe ., ., . und . thematisiert. Anschlie?end erfolgt eine Einordnung der vo作者: 樹(shù)木心 時(shí)間: 2025-3-26 17:09
Conference proceedings 2023y 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 organi作者: FUSC 時(shí)間: 2025-3-26 21:56 作者: 營(yíng)養(yǎng) 時(shí)間: 2025-3-27 04:51 作者: 創(chuàng)造性 時(shí)間: 2025-3-27 06:13 作者: Juvenile 時(shí)間: 2025-3-27 13:12
Coupling User Preference with?External Rewards to?Enable Driver-centered and?Resource-aware EV Chargndation approach that would allow each EV driver to strike an optimal balance between the user preference (historical charging pattern) and the external reward (driving distance and wait time). Experimental results on two real-world datasets demonstrate the unique features and superior performance of our approach to the competing methods.作者: adduction 時(shí)間: 2025-3-27 15:22 作者: oblique 時(shí)間: 2025-3-27 19:47 作者: insurgent 時(shí)間: 2025-3-27 23:26 作者: 甜食 時(shí)間: 2025-3-28 03:59
DistSPECTRL: Distributing Specifications in?Multi-Agent Reinforcement Learning Systemses that allow agents to operate in a coordination-free manner for local objectives, while using a decentralized communication protocol for enforcing global ones. Experimental results support our claim that sophisticated multi-agent distributed planning problems can be effectively realized using specification-guided learning. Code is provided at ..作者: Infirm 時(shí)間: 2025-3-28 09:37 作者: EXPEL 時(shí)間: 2025-3-28 13:18
https://doi.org/10.1007/978-3-031-26412-2artificial intelligence; autonomous agents; computer networks; computer security; computer systems; corre作者: Apogee 時(shí)間: 2025-3-28 17:10
Lecture Notes in Computer Sciencehttp://image.papertrans.cn/m/image/620512.jpg作者: palliate 時(shí)間: 2025-3-28 22:31 作者: Inoperable 時(shí)間: 2025-3-28 23:36 作者: 暗語(yǔ) 時(shí)間: 2025-3-29 06:07 作者: 捏造 時(shí)間: 2025-3-29 08:30
Massih-Reza Amini,Stéphane Canu,Grigorios Tsoumaka作者: 使糾纏 時(shí)間: 2025-3-29 12:15 作者: 反抗者 時(shí)間: 2025-3-29 18:58 作者: BAIL 時(shí)間: 2025-3-29 22:20 作者: entrance 時(shí)間: 2025-3-30 01:25
Jiajing Ling,Arambam James Singh,Nguyen Duc Thien,Akshat Kumar作者: 成績(jī)上升 時(shí)間: 2025-3-30 05:25 作者: transplantation 時(shí)間: 2025-3-30 10:36 作者: Pde5-Inhibitors 時(shí)間: 2025-3-30 13:07 作者: TAP 時(shí)間: 2025-3-30 17:56
Safe Exploration Method for?Reinforcement Learning Under Existence of?Disturbancesufficient conditions to construct conservative inputs not containing an exploring aspect used in the proposed method and prove that the safety in the above explained sense is guaranteed with the proposed method. Furthermore, we illustrate the validity and effectiveness of the proposed method throug作者: GENRE 時(shí)間: 2025-3-31 00:39
Model Selection in?Reinforcement Learning with?General Function Approximationson classes (i.e., . and .) a priori. Furthermore, for both the settings, we show that the cost of model selection is an additive term in the regret having weak (logarithmic) dependence on the learning horizon?..作者: Genistein 時(shí)間: 2025-3-31 01:02 作者: 黃油沒(méi)有 時(shí)間: 2025-3-31 08:22 作者: 卷發(fā) 時(shí)間: 2025-3-31 12:25 作者: 動(dòng)脈 時(shí)間: 2025-3-31 13:46
MAVIPER: Learning Decision Tree Policies for?Interpretable Multi-agent Reinforcement Learninge trees of each agent by predicting the behavior of the other agents using their anticipated trees, and uses resampling to focus on states that are critical for its interactions with other agents. We show that both algorithms generally outperform the baselines and that MAVIPER-trained agents achieve
