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Found 6 papers, 0 papers with code
Relative Lipschitzness in Extragradient Methods and a Direct Recipe for Acceleration
We show that standard extragradient methods (i. e. mirror prox and dual extrapolation) recover optimal accelerated rates for first-order minimization of smooth convex functions.
Sparsity, variance and curvature in multi-armed bandits
In (online) learning theory the concepts of sparsity, variance and curvature are well-understood and are routinely used to obtain refined regret and generalization bounds.
Input Sparsity Time Low-Rank Approximation via Ridge Leverage Score Sampling
Our method is based on a recursive sampling scheme for computing a representative subset of $A$'s columns, which is then used to find a low-rank approximation.
Optimal approximate matrix product in terms of stable rank
We prove, using the subspace embedding guarantee in a black box way, that one can achieve the spectral norm guarantee for approximate matrix multiplication with a dimensionality-reducing map having $m = O(\tilde{r}/\varepsilon^2)$ rows.
Dimensionality Reduction for k-Means Clustering and Low Rank Approximation
We show how to approximate a data matrix $\mathbf{A}$ with a much smaller sketch $\mathbf{\tilde A}$ that can be used to solve a general class of constrained k-rank approximation problems to within $(1+\epsilon)$ error.
Uniform Sampling for Matrix Approximation
In addition to an improved understanding of uniform sampling, our main proof introduces a structural result of independent interest: we show that every matrix can be made to have low coherence by reweighting a small subset of its rows.
