no code implementations • 19 Mar 2023 • Chen Zhang, Junhui Gao, Lingxin Kong, Guangshuo cao, Xiangyu Guo, Wei Liu
Spatial transcriptomic (ST) clustering employs spatial and transcription information to group spots spatially coherent and transcriptionally similar together into the same spatial domain.
1 code implementation • NeurIPS 2021 • Chen Ma, Xiangyu Guo, Li Chen, Jun-Hai Yong, Yisen Wang
In this paper, we propose a novel geometric-based approach called Tangent Attack (TA), which identifies an optimal tangent point of a virtual hemisphere located on the decision boundary to reduce the distortion of the attack.
no code implementations • 19 Oct 2020 • Di Wang, Xiangyu Guo, Chaowen Guan, Shi Li, Jinhui Xu
To the best of our knowledge, this is the first work that studies and provides theoretical guarantees for the stochastic linear combination of non-linear regressions model.
no code implementations • 19 Oct 2020 • Di Wang, Xiangyu Guo, Shi Li, Jinhui Xu
In this paper, we study the problem of estimating latent variable models with arbitrarily corrupted samples in high dimensional space ({\em i. e.,} $d\gg n$) where the underlying parameter is assumed to be sparse.
1 code implementation • 13 Aug 2020 • Xiangyu Guo, Janardhan Kulkarni, Shi Li, Jiayi Xian
In this paper we introduce and study the online consistent $k$-clustering with outliers problem, generalizing the non-outlier version of the problem studied in [Lattanzi-Vassilvitskii, ICML17].
no code implementations • NeurIPS 2018 • Shi Li, Xiangyu Guo
In this paper, we improve the number of outliers to the best possible $(1+\epsilon)z$, while maintaining the $O(1)$-approximation ratio and independence of communication cost on $z$.
1 code implementation • NeurIPS 2018 • Xiangyu Guo, Shi Li
In this paper, we improve the number of outliers to the best possible $(1+\epsilon)z$, while maintaining the $O(1)$-approximation ratio and independence of communication cost on $z$.