Zobrazeno 1 - 10
of 4 672
pro vyhledávání: '"Xiao, Quan"'
Autor:
Xiao, Quan, Chen, Tianyi
Bilevel optimization has witnessed a resurgence of interest, driven by its critical role in trustworthy and efficient machine learning applications. Recent research has focused on proposing efficient methods with provable convergence guarantees. Howe
Externí odkaz:
http://arxiv.org/abs/2408.16087
Autor:
Jiang, Liuyuan, Xiao, Quan, Tenorio, Victor M., Real-Rojas, Fernando, Marques, Antonio G., Chen, Tianyi
Interest in bilevel optimization has grown in recent years, partially due to its applications to tackle challenging machine-learning problems. Several exciting recent works have been centered around developing efficient gradient-based algorithms that
Externí odkaz:
http://arxiv.org/abs/2406.10148
In this paper, we derive the Carrollian amplitude in the framework of bulk reduction. The Carrollian amplitude is shown to relate to the scattering amplitude by a Fourier transform in this method. We propose Feynman rules to calculate the Carrollian
Externí odkaz:
http://arxiv.org/abs/2402.04120
In this paper, we introduce a bilevel optimization framework for addressing inverse mean-field games, alongside an exploration of numerical methods tailored for this bilevel problem. The primary benefit of our bilevel formulation lies in maintaining
Externí odkaz:
http://arxiv.org/abs/2401.05539
Bilevel optimization has recently regained interest owing to its applications in emerging machine learning fields such as hyperparameter optimization, meta-learning, and reinforcement learning. Recent results have shown that simple alternating (impli
Externí odkaz:
http://arxiv.org/abs/2306.02422
Bilevel optimization enjoys a wide range of applications in hyper-parameter optimization, meta-learning and reinforcement learning. However, bilevel optimization problems are difficult to solve. Recent progress on scalable bilevel algorithms mainly f
Externí odkaz:
http://arxiv.org/abs/2302.05185
Autor:
Xiong-Ying Pu, Lu Chen, Hao Hu, Qian Wu, Wen-Hao Jiang, Jin-Ling Lu, Huan-Huan Chen, Xiao-Quan Xu, Fei-Yun Wu
Publikováno v:
Insights into Imaging, Vol 15, Iss 1, Pp 1-9 (2024)
Abstract Objective To investigate the value of Dixon magnetic resonance imaging (MRI)-based quantitative parameters of extraocular muscles (EOMs), intraorbital fat (IF), and lacrimal glands (LGs) in staging patients with thyroid-associated ophthalmop
Externí odkaz:
https://doaj.org/article/b76408fc37814674a4ad8430b7984770
Publikováno v:
Journal of High Energy Physics, Vol 2024, Iss 5, Pp 1-66 (2024)
Abstract In this paper, we derive the Carrollian amplitude in the framework of bulk reduction. The Carrollian amplitude is shown to relate to the scattering amplitude by a Fourier transform in this method. We propose Feynman rules to calculate the Ca
Externí odkaz:
https://doaj.org/article/9fabc8f466b248658c3e995349fe13b7
Stochastic bilevel optimization, which captures the inherent nested structure of machine learning problems, is gaining popularity in many recent applications. Existing works on bilevel optimization mostly consider either unconstrained problems or con
Externí odkaz:
http://arxiv.org/abs/2211.07096