Zobrazeno 1 - 10
of 532
pro vyhledávání: '"Lyu, Xin"'
Autor:
Cheng, Qiyuan, XIong, Jianping, Ding, Xu, Ji, Kaifan, Li, Jiao, Liu, Chao, Li, Jiangdan, Luo, Jingxiao, Lyu, Xin, Han, Zhanwen, Chen, Xuefei
Low mass-ratio (q) contact binary systems are progenitors of stellar mergers such as blue straggles (BS) or fast-rotating FK Com stars. In this study, we present the first light curve analysis of two newly identified low mass-ratio contact binary sys
Externí odkaz:
http://arxiv.org/abs/2405.19841
Autor:
Zhang, Fan, Wang, Zhaohan, Lyu, Xin, Zhao, Siyuan, Li, Mengjian, Geng, Weidong, Ji, Naye, Du, Hui, Gao, Fuxing, Wu, Hao, Li, Shunman
Speech-driven gesture generation is an emerging field within virtual human creation. However, a significant challenge lies in accurately determining and processing the multitude of input features (such as acoustic, semantic, emotional, personality, a
Externí odkaz:
http://arxiv.org/abs/2403.10805
One of the most basic problems for studying the "price of privacy over time" is the so called private counter problem, introduced by Dwork et al. (2010) and Chan et al. (2010). In this problem, we aim to track the number of events that occur over tim
Externí odkaz:
http://arxiv.org/abs/2403.00028
We study the cost of parallelizing weak-to-strong boosting algorithms for learning, following the recent work of Karbasi and Larsen. Our main results are two-fold: - First, we prove a tight lower bound, showing that even "slight" parallelization of b
Externí odkaz:
http://arxiv.org/abs/2402.15145
In this paper, we investigate the thermodynamic properties of charged AdS Gauss-Bonnet black holes and the associations with the Lyapunov exponent. The chaotic features of the black holes and the isobaric heat capacity characterized by Lyapunov expon
Externí odkaz:
http://arxiv.org/abs/2312.11912
The Private Aggregation of Teacher Ensembles (PATE) framework is a versatile approach to privacy-preserving machine learning. In PATE, teacher models that are not privacy-preserving are trained on distinct portions of sensitive data. Privacy-preservi
Externí odkaz:
http://arxiv.org/abs/2312.02132
In his breakthrough paper, Raz showed that any parity learning algorithm requires either quadratic memory or an exponential number of samples [FOCS'16, JACM'19]. A line of work that followed extended this result to a large class of learning problems.
Externí odkaz:
http://arxiv.org/abs/2310.08070
Autor:
Cohen, Edith, Lyu, Xin
We propose the \emph{Target Charging Technique} (TCT), a unified privacy analysis framework for interactive settings where a sensitive dataset is accessed multiple times using differentially private algorithms. Unlike traditional composition, where p
Externí odkaz:
http://arxiv.org/abs/2302.11044
Autor:
Lyu, Xin
We show a new PRG construction fooling depth-$d$, size-$m$ $\mathsf{AC}^0$ circuits within error $\varepsilon$, which has seed length $O(\log^{d-1}(m)\log(m/\varepsilon)\log\log(m))$. Our PRG improves on previous work (Trevisan and Xue 2013, Servedio
Externí odkaz:
http://arxiv.org/abs/2301.10102
Composition theorems are general and powerful tools that facilitate privacy accounting across multiple data accesses from per-access privacy bounds. However they often result in weaker bounds compared with end-to-end analysis. Two popular tools that
Externí odkaz:
http://arxiv.org/abs/2211.12063