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Found 7 papers, 1 papers with code
SequentialAttention++ for Block Sparsification: Differentiable Pruning Meets Combinatorial Optimization
Neural network pruning is a key technique towards engineering large yet scalable, interpretable, and generalizable models.
Performance of $\ell_1$ Regularization for Sparse Convex Optimization
We give the first recovery guarantees for the Group LASSO for sparse convex optimization with vector-valued features.
Sharper Bounds for $\ell_p$ Sensitivity Sampling
In this work, we show the first bounds for sensitivity sampling for $\ell_p$ subspace embeddings for $p > 2$ that improve over the general $\mathfrak S d$ bound, achieving a bound of roughly $\mathfrak S^{2-2/p}$ for $2<p<\infty$.
Sequential Attention for Feature Selection
Feature selection is the problem of selecting a subset of features for a machine learning model that maximizes model quality subject to a budget constraint.
Online Lewis Weight Sampling
Towards our result, we give the first analysis of "one-shot'' Lewis weight sampling of sampling rows proportionally to their Lewis weights, with sample complexity $\tilde O(d^{p/2}/\epsilon^2)$ for $p>2$.
Active Linear Regression for $\ell_p$ Norms and Beyond
By combining this with our techniques for $\ell_p$ regression, we obtain an active regression algorithm making $\tilde O(d^{1+\max\{1, p/2\}}/\mathrm{poly}(\epsilon))$ queries for such loss functions, including the Tukey and Huber losses, answering another question of [CD21].
Tight Kernel Query Complexity of Kernel Ridge Regression and Kernel $k$-means Clustering
We present tight lower bounds on the number of kernel evaluations required to approximately solve kernel ridge regression (KRR) and kernel $k$-means clustering (KKMC) on $n$ input points.
