Search Results for author:
Found 7 papers, 3 papers with code
STGIC: a graph and image convolution-based method for spatial transcriptomic clustering
Spatial transcriptomic (ST) clustering employs spatial and transcription information to group spots spatially coherent and transcriptionally similar together into the same spatial domain.
Finding Optimal Tangent Points for Reducing Distortions of Hard-label Attacks
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.
Estimating Stochastic Linear Combination of Non-linear Regressions Efficiently and Scalably
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.
Robust High Dimensional Expectation Maximization Algorithm via Trimmed Hard Thresholding
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.
Consistent $k$-Median: Simpler, Better and Robust
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].
Distributed k-Clustering for Data with Heavy Noise
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 $z$.
Distributed $k$-Clustering for Data with Heavy Noise
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 $z$.
