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Found 7 papers, 0 papers with code
Private graphon estimation via sum-of-squares
We develop the first pure node-differentially-private algorithms for learning stochastic block models and for graphon estimation with polynomial running time for any constant number of blocks.
Computational-Statistical Gaps for Improper Learning in Sparse Linear Regression
In particular, we show that an improper learning algorithm for sparse linear regression can be used to solve sparse PCA problems (with a negative spike) in their Wishart form, in regimes in which efficient algorithms are widely believed to require at least $\Omega(k^2)$ samples.
Reaching Kesten-Stigum Threshold in the Stochastic Block Model under Node Corruptions
We study robust community detection in the context of node-corrupted stochastic block model, where an adversary can arbitrarily modify all the edges incident to a fraction of the $n$ vertices.
SQ Lower Bounds for Random Sparse Planted Vector Problem
A classical question is how to recover this planted vector given a random basis in this subspace.
Fast algorithm for overcomplete order-3 tensor decomposition
We develop the first fast spectral algorithm to decompose a random third-order tensor over $\mathbb{R}^d$ of rank up to $O(d^{3/2}/\text{polylog}(d))$.
Robust recovery for stochastic block models
We develop an efficient algorithm for weak recovery in a robust version of the stochastic block model.
Estimating Rank-One Spikes from Heavy-Tailed Noise via Self-Avoiding Walks
For the case of Gaussian noise, the top eigenvector of the given matrix is a widely-studied estimator known to achieve optimal statistical guarantees, e. g., in the sense of the celebrated BBP phase transition.
