Zobrazeno 1 - 10
of 153
pro vyhledávání: '"Ma Yuheng"'
Publikováno v:
ICML2024 Proceedings
Previous studies yielded discouraging results for item-level locally differentially private linear regression with $s^*$-sparsity assumption, where the minimax rate for $nm$ samples is $\mathcal{O}(s^{*}d / nm\varepsilon^2)$. This can be challenging
Externí odkaz:
http://arxiv.org/abs/2408.04313
While extensive research has explored the use of large language models (LLMs) for table-based reasoning, most approaches struggle with scalability when applied to large tables. To maintain the superior comprehension abilities of LLMs in these scenari
Externí odkaz:
http://arxiv.org/abs/2407.03061
We initiate the study of locally differentially private (LDP) learning with public features. We define semi-feature LDP, where some features are publicly available while the remaining ones, along with the label, require protection under local differe
Externí odkaz:
http://arxiv.org/abs/2405.13481
We consider the paradigm of unsupervised anomaly detection, which involves the identification of anomalies within a dataset in the absence of labeled examples. Though distance-based methods are top-performing for unsupervised anomaly detection, they
Externí odkaz:
http://arxiv.org/abs/2312.01046
Autor:
Ma, Yuheng, Yang, Hanfang
In this work, we investigate the problem of public data assisted non-interactive Local Differentially Private (LDP) learning with a focus on non-parametric classification. Under the posterior drift assumption, we for the first time derive the mini-ma
Externí odkaz:
http://arxiv.org/abs/2311.11369
Nonlocality brings many challenges to the implementation of finite element methods (FEM) for nonlocal problems, such as large number of queries and invoke operations on the meshes. Besides, the interactions are usually limited to Euclidean balls, so
Externí odkaz:
http://arxiv.org/abs/2302.07499
Autor:
Yang, Zijiang1 (AUTHOR), Ma, Yuheng1,2 (AUTHOR), Jing, Qi1 (AUTHOR) jingqi@bjut.edu.cn, Ren, Zhongyu1 (AUTHOR)
Publikováno v:
Polymers (20734360). Dec2024, Vol. 16 Issue 23, p3271. 17p.
Publikováno v:
In Geoenergy Science and Engineering January 2025 244
Autor:
Wang, Xiaojuan, Liu, Xin, Lu, Wei, Ma, Yuheng, Wang, Xue, Xiao, Lufei, Hu, Zhangjun, Liu, Zhengjie, Zhu, Yingzhong, Kong, Lin
Publikováno v:
In Dyes and Pigments February 2025 233
In this paper we consider a linearized variable-time-step two-step backward differentiation formula (BDF2) scheme for solving nonlinear parabolic equations. The scheme is constructed by using the variable time-step BDF2 for the linear term and a Newt
Externí odkaz:
http://arxiv.org/abs/2201.06008