Zobrazeno 1 - 10
of 146
pro vyhledávání: '"Tang, Ziyang"'
Randomized experiments (a.k.a. A/B tests) are a powerful tool for estimating treatment effects, to inform decisions making in business, healthcare and other applications. In many problems, the treatment has a lasting effect that evolves over time. A
Externí odkaz:
http://arxiv.org/abs/2210.07536
Conformal prediction is a simple and powerful tool that can quantify uncertainty without any distributional assumptions. Many existing methods only address the average coverage guarantee, which is not ideal compared to the stronger conditional covera
Externí odkaz:
http://arxiv.org/abs/2206.13092
Publikováno v:
In Talanta 1 November 2024 279
We consider offline Imitation Learning from corrupted demonstrations where a constant fraction of data can be noise or even arbitrary outliers. Classical approaches such as Behavior Cloning assumes that demonstrations are collected by an presumably o
Externí odkaz:
http://arxiv.org/abs/2201.12594
Reinforcement learning (RL) has drawn increasing interests in recent years due to its tremendous success in various applications. However, standard RL algorithms can only be applied for single reward function, and cannot adapt to an unseen reward fun
Externí odkaz:
http://arxiv.org/abs/2201.00236
Autor:
Budhkar, Aishwarya1 (AUTHOR), Tang, Ziyang2 (AUTHOR), Liu, Xiang3 (AUTHOR), Zhang, Xuhong1 (AUTHOR) zhangxuh@iu.edu, Su, Jing3,4 (AUTHOR) zhangxuh@iu.edu, Song, Qianqian5 (AUTHOR) zhangxuh@iu.edu
Publikováno v:
Briefings in Bioinformatics. Sep2024, Vol. 25 Issue 5, p1-12. 12p.
Syntheses, crystal structures and magnetic properties of four-coordinate Ni(II) and Cu(II) complexes
Publikováno v:
In Journal of Molecular Structure 15 August 2024 1310
Autor:
Zhang, Tengkun, Xu, Hongjuan, Tang, Ziyang, Cui, Hui-Hui, Wang, Jin, Wang, Miao, Li, Zhaoqian
Publikováno v:
In Solid State Sciences June 2024 152
Off-policy evaluation (OPE) is the task of estimating the expected reward of a given policy based on offline data previously collected under different policies. Therefore, OPE is a key step in applying reinforcement learning to real-world domains suc
Externí odkaz:
http://arxiv.org/abs/2103.05741
Autor:
Shen, Tongyang, Xiang, Yanhong, Zhang, Huiyu, Liu, Jian, Zou, Qiuling, wu, Tong, Tang, Ziyang, Liu, Guanyu, Tan, Yucong, Xiong, Lizhi, Li, Jian
Publikováno v:
In Ceramics International November 2024