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pro vyhledávání: '"Lin QIAN"'
Offline Multi-Agent Reinforcement Learning (MARL) is an emerging field that aims to learn optimal multi-agent policies from pre-collected datasets. Compared to single-agent case, multi-agent setting involves a large joint state-action space and coupl
Externí odkaz:
http://arxiv.org/abs/2412.07639
Autor:
Blin, Anna, Kolar, Alexander, Kamen, Andrew, Lin, Qian, Liu, Xiaogang, Benamrouche, Aziz, Bachelet, Romain, Goldner, Philippe, Zhong, Tian, Serrano, Diana, Tallaire, Alexandre
The obtention of quantum-grade rare-earth doped oxide thin films that can be integrated with optical cavities and microwave resonators is of great interest for the development of scalable quantum devices. Among the different growth methods, Chemical
Externí odkaz:
http://arxiv.org/abs/2411.10196
This paper aims to discuss the impact of random initialization of neural networks in the neural tangent kernel (NTK) theory, which is ignored by most recent works in the NTK theory. It is well known that as the network's width tends to infinity, the
Externí odkaz:
http://arxiv.org/abs/2410.05626
In recent years, significant progress has been made in multi-objective reinforcement learning (RL) research, which aims to balance multiple objectives by incorporating preferences for each objective. In most existing studies, specific preferences mus
Externí odkaz:
http://arxiv.org/abs/2409.09958
Building on recent studies of large-dimensional kernel regression, particularly those involving inner product kernels on the sphere $\mathbb{S}^{d}$, we investigate the Pinsker bound for inner product kernel regression in such settings. Specifically,
Externí odkaz:
http://arxiv.org/abs/2409.00915
Autor:
Li, Yicheng, Lin, Qian
It is well known that eigenfunctions of a kernel play a crucial role in kernel regression. Through several examples, we demonstrate that even with the same set of eigenfunctions, the order of these functions significantly impacts regression outcomes.
Externí odkaz:
http://arxiv.org/abs/2409.00894
The saturation effect refers to the phenomenon that the kernel ridge regression (KRR) fails to achieve the information theoretical lower bound when the smoothness of the underground truth function exceeds certain level. The saturation effect has been
Externí odkaz:
http://arxiv.org/abs/2405.09362
The generalization ability of kernel interpolation in large dimensions (i.e., $n \asymp d^{\gamma}$ for some $\gamma>0$) might be one of the most interesting problems in the recent renaissance of kernel regression, since it may help us understand the
Externí odkaz:
http://arxiv.org/abs/2404.12597
Autor:
Su, Chenguang, Lin, Qian, Kong, Linqquan, Chen, Shi, Moharrami, Kimiya, Zheng, Yangheng, Li, Jin
In this study, we conducted a comprehensive characterization and optimization of a cryogenic pure CsI (pCsI) detector. We utilized a \SI{2}{\centi\metre} cubic crystal coupled with a HAMAMATSU R11065 photomultiplier tube (PMT), achieving a remarkable
Externí odkaz:
http://arxiv.org/abs/2402.05026
Kernel methods are widely used in machine learning, especially for classification problems. However, the theoretical analysis of kernel classification is still limited. This paper investigates the statistical performances of kernel classifiers. With
Externí odkaz:
http://arxiv.org/abs/2402.01148