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pro vyhledávání: '"Zhu, Rong"'
Autor:
Zhu, Rong J. B., Qiu, Yanqi
We study best-arm identification (BAI) in the fixed-budget setting. Adaptive allocations based on upper confidence bounds (UCBs), such as UCBE, are known to work well in BAI. However, it is well-known that its optimal regret is theoretically dependen
Externí odkaz:
http://arxiv.org/abs/2408.04869
Autor:
Lu, Jiaming, Zhu, Rong J. B.
In high-dimensional settings, Bayesian optimization (BO) can be expensive and infeasible. The random embedding Bayesian optimization algorithm is commonly used to address high-dimensional BO challenges. However, this method relies on the effective su
Externí odkaz:
http://arxiv.org/abs/2408.04860
Recent speech enhancement methods based on convolutional neural networks (CNNs) and transformer have been demonstrated to efficaciously capture time-frequency (T-F) information on spectrogram. However, the correlation of each channels of speech featu
Externí odkaz:
http://arxiv.org/abs/2407.06524
Autor:
Zeng, Tianjing, Lan, Junwei, Ma, Jiahong, Wei, Wenqing, Zhu, Rong, Li, Pengfei, Ding, Bolin, Lian, Defu, Wei, Zhewei, Zhou, Jingren
Cardinality estimation (CardEst) is essential for optimizing query execution plans. Recent ML-based CardEst methods achieve high accuracy but face deployment challenges due to high preparation costs and lack of transferability across databases. In th
Externí odkaz:
http://arxiv.org/abs/2406.01027
Autor:
Qian, Yichen, He, Yongyi, Zhu, Rong, Huang, Jintao, Ma, Zhijian, Wang, Haibin, Wang, Yaohua, Sun, Xiuyu, Lian, Defu, Ding, Bolin, Zhou, Jingren
Designing effective data manipulation methods is a long standing problem in data lakes. Traditional methods, which rely on rules or machine learning models, require extensive human efforts on training data collection and tuning models. Recent methods
Externí odkaz:
http://arxiv.org/abs/2405.06510
Autor:
Zhu, Rong J. B., Jiang, Weiwei
Treatment effects in regression discontinuity designs (RDDs) are often estimated using local regression methods. However, global approximation methods are generally deemed inefficient. In this paper, we propose a semiparametric framework tailored for
Externí odkaz:
http://arxiv.org/abs/2403.05803
Autor:
CLARK, ANDREW E., ZHU, RONG
Publikováno v:
AQ: Australian Quarterly, 2024 Jul 01. 95(3), 20-22.
Externí odkaz:
https://www.jstor.org/stable/27311307
Publikováno v:
PVLDB, 17(2): 197 - 210, 2023
For efficient query processing, DBMS query optimizers have for decades relied on delicate cardinality estimation methods. In this work, we propose an Attention-based LEarned Cardinality Estimator (ALECE for short) for SPJ queries. The core idea is to
Externí odkaz:
http://arxiv.org/abs/2310.05349
Publikováno v:
Polish Journal of Microbiology, Vol 73, Iss 3, Pp 395-401 (2024)
A novel virus, temporarily named “Arctic wolf parvovirus” (AWPV), was discovered in a pharyngeal metagenomic library derived from an Arctic wolf (Canis lupus arctos) in China. The genome sequence was assigned GenBase accession number C_AA071902.1
Externí odkaz:
https://doaj.org/article/99afb6aca53047eb88996cac43eaf988