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Refinado por: assunto: Computer Science - Learning remover assunto: Learning remover
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On the Approximation of Cooperative Heterogeneous Multi-Agent Reinforcement Learning (MARL) using Mean Field Control (MFC)
On the Approximation of Cooperative Heterogeneous Multi-Agent Reinforcement Learning (MARL) using Mean Field Control (MFC)
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On the Approximation of Cooperative Heterogeneous Multi-Agent Reinforcement Learning (MARL) using Mean Field Control (MFC)

Washim Uddin Mondal ; Agarwal, Mridul ; Aggarwal, Vaneet ; Ukkusuri, Satish V

arXiv.org, 2022-01 [Periódico revisado por pares]

Ithaca: Cornell University Library, arXiv.org

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2
Chronosymbolic Learning: Efficient CHC Solving with Symbolic Reasoning and Inductive Learning
Chronosymbolic Learning: Efficient CHC Solving with Symbolic Reasoning and Inductive Learning
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Chronosymbolic Learning: Efficient CHC Solving with Symbolic Reasoning and Inductive Learning

Luo, Ziyan ; Si, Xujie

arXiv.org, 2024-06

Ithaca: Cornell University Library, arXiv.org

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3
USE: Dynamic User Modeling with Stateful Sequence Models
USE: Dynamic User Modeling with Stateful Sequence Models
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USE: Dynamic User Modeling with Stateful Sequence Models

Zhou, Zhihan ; Fang, Qixiang ; Neves, Leonardo ; Barbieri, Francesco ; Liu, Yozen ; Liu, Han ; Bos, Maarten W ; Dotsch, Ron

arXiv.org, 2024-03

Ithaca: Cornell University Library, arXiv.org

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4
Independent and Decentralized Learning in Markov Potential Games
Independent and Decentralized Learning in Markov Potential Games
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Independent and Decentralized Learning in Markov Potential Games

Maheshwari, Chinmay ; Wu, Manxi ; Pai, Druv ; Sastry, Shankar

arXiv.org, 2023-11

Ithaca: Cornell University Library, arXiv.org

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5
On the Complexity of Learning to Cooperate with Populations of Socially Rational Agents
On the Complexity of Learning to Cooperate with Populations of Socially Rational Agents
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On the Complexity of Learning to Cooperate with Populations of Socially Rational Agents

Loftin, Robert ; Bandyopadhyay, Saptarashmi ; Mustafa Mert Çelikok

arXiv.org, 2024-06

Ithaca: Cornell University Library, arXiv.org

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6
A Semi-supervised Sensing Rate Learning based CMAB Scheme to Combat COVID-19 by Trustful Data Collection in the Crowd
A Semi-supervised Sensing Rate Learning based CMAB Scheme to Combat COVID-19 by Trustful Data Collection in the Crowd
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A Semi-supervised Sensing Rate Learning based CMAB Scheme to Combat COVID-19 by Trustful Data Collection in the Crowd

Tang, Jianheng ; Fan, Kejia ; Xie, Wenxuan ; Zeng, Luomin ; Han, Feijiang ; Huang, Guosheng ; Wang, Tian ; Liu, Anfeng ; Zhang, Shaobo

arXiv.org, 2023-06

Ithaca: Cornell University Library, arXiv.org

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7
Making Language Models Better Tool Learners with Execution Feedback
Making Language Models Better Tool Learners with Execution Feedback
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Making Language Models Better Tool Learners with Execution Feedback

Qiao, Shuofei ; Gui, Honghao ; Lv, Chengfei ; Jia, Qianghuai ; Chen, Huajun ; Zhang, Ningyu

arXiv.org, 2024-03

Ithaca: Cornell University Library, arXiv.org

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8
MizAR 60 for Mizar 50
MizAR 60 for Mizar 50
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MizAR 60 for Mizar 50

Jakubův, Jan ; Chvalovský, Karel ; Goertzel, Zarathustra ; Kaliszyk, Cezary ; Olšák, Mirek ; Piotrowski, Bartosz ; Schulz, Stephan ; Suda, Martin ; Urban, Josef

arXiv.org, 2023-03

Ithaca: Cornell University Library, arXiv.org

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9
Learning temporal formulas from examples is hard
Learning temporal formulas from examples is hard
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Learning temporal formulas from examples is hard

Mascle, Corto ; Fijalkow, Nathanaël ; Lagarde, Guillaume

arXiv.org, 2023-12

Ithaca: Cornell University Library, arXiv.org

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10
Learning big logical rules by joining small rules
Learning big logical rules by joining small rules
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Learning big logical rules by joining small rules

Hocquette, Céline ; Niskanen, Andreas ; Morel, Rolf ; Järvisalo, Matti ; Cropper, Andrew

arXiv.org, 2024-01

Ithaca: Cornell University Library, arXiv.org

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