constant probability
Low-degree evidence for computational transition of recovery rate in stochastic block model
We investigate implications of the (extended) low-degree conjecture (recently formalized in [moitra et al2023]) in the context of the symmetric stochastic block model. Assuming the conjecture holds, we establish that no polynomial-time algorithm can weakly recover community labels below the Kesten-Stigum (KS) threshold. In particular, we rule out polynomial-time estimators that, with constant probability, achieve $n^{-0.49}$
Semi-supervised Active Linear Regression
Labeled data often comes at a high cost as it may require recruiting human labelers or running costly' experiments. At the same time, in many practical scenarios, one already has access to a partially labeled, potentially biased dataset that can help with the learning task at hand. Motivated by such settings, we formally initiate a study of semi-supervised active learning through the frame of linear regression.
Hardness of Low Rank Approximation of Entrywise Transformed Matrix Products
Some related lower bounds include the work of Backurs et al. [2017] that solving kernel Support V ector Machines (SVM), ridge regression, or Principal Component Analysis (PCA) problems to high accuracy or approximating kernel density estimates up to a constant factor for kernels with
Sketching Algorithms for Sparse Dictionary Learning: PTAS and Turnstile Streaming
Classically, sketching has been applied to design low-memory algorithms in the streaming setting, when the input is presented to the algorithm as a sequence of updates.
Sketching Algorithms for Sparse Dictionary Learning: PTAS and Turnstile Streaming
Classically, sketching has been applied to design low-memory algorithms in the streaming setting, when the input is presented to the algorithm as a sequence of updates.
