Zobrazeno 1 - 10
of 392
pro vyhledávání: '"Li, Yingru"'
Foundation models often struggle with uncertainty when faced with new situations in online decision-making, necessitating scalable and efficient exploration to resolve this uncertainty. We introduce GPT-HyperAgent, an augmentation of GPT with HyperAg
Externí odkaz:
http://arxiv.org/abs/2407.13195
Autor:
Li, Yingru, Luo, Zhi-Quan
This work advances randomized exploration in reinforcement learning (RL) with function approximation modeled by linear mixture MDPs. We establish the first prior-dependent Bayesian regret bound for RL with function approximation; and refine the Bayes
Externí odkaz:
http://arxiv.org/abs/2403.11175
The dynamic competition between radar and jammer systems presents a significant challenge for modern Electronic Warfare (EW), as current active learning approaches still lack sample efficiency and fail to exploit jammer's characteristics. In this pap
Externí odkaz:
http://arxiv.org/abs/2402.16274
Autor:
Li, Yingru
We introduce the first probabilistic framework tailored for sequential random projection, an approach rooted in the challenges of sequential decision-making under uncertainty. The analysis is complicated by the sequential dependence and high-dimensio
Externí odkaz:
http://arxiv.org/abs/2402.14026
Autor:
Li, Yingru
We present a simplified and unified analysis of the Johnson-Lindenstrauss (JL) lemma, a cornerstone of dimensionality reduction for managing high-dimensional data. Our approach simplifies understanding and unifies various constructions under the JL f
Externí odkaz:
http://arxiv.org/abs/2402.10232
This work tackles the complexities of multi-player scenarios in \emph{unknown games}, where the primary challenge lies in navigating the uncertainty of the environment through bandit feedback alongside strategic decision-making. We introduce Thompson
Externí odkaz:
http://arxiv.org/abs/2402.09456
We propose HyperAgent, a reinforcement learning (RL) algorithm based on the hypermodel framework for exploration in RL. HyperAgent allows for the efficient incremental approximation of posteriors associated with an optimal action-value function ($Q^\
Externí odkaz:
http://arxiv.org/abs/2402.10228
Publikováno v:
In International Immunopharmacology 10 September 2024 138
Autor:
Wang, Yuqi, Zhao, Wei, Li, Mingpu, Zhuo, Qing, Li, Yuanyuan, Tan, Linli, Li, Yingru, Dong, Hangyu, Long, Qiong
Publikováno v:
In Materials Today Communications August 2024 40
Autor:
Xie, Shule, Li, Yingru, Mai, Lianxi, Gao, Xiaolin, Huang, Guoxin, Sun, Wenhao, Qiao, Liang, Li, Bowen, Wang, Youyuan, Lin, Zhaoyu
Publikováno v:
In Cancer Letters 1 November 2024 604