T. Yasuda

activity

20192025

most citedSequential Attention for Feature Selection

2 citations · 7 across the 18 of their papers we have counts for

collaborators

Showing 2021Show all

3 papers · 1 filter

cs.LG2021

Active Linear Regression for $ℓ_{p}$ Norms and Beyond

Cameron Musco, Christopher Musco, David P. Woodruff +1

We study active sampling algorithms for linear regression, which aim to query only a few entries of a target vector $b \in R^{n}$ and output a near minimizer to $\min_{x\in\mat…

cs.DS2021

Improved Algorithms for Low Rank Approximation from Sparsity

David P. Woodruff, Taisuke Yasuda

We overcome two major bottlenecks in the study of low rank approximation by assuming the low rank factors themselves are sparse. Specifically, (1) for low rank approximation with s…

cs.DS2021

Exponentially Improved Dimensionality Reduction for $ℓ_{1}$ : Subspace Embeddings and Independence Testing

Yi Li, David P. Woodruff, Taisuke Yasuda

Despite many applications, dimensionality reduction in the $ℓ_{1}$ -norm is much less understood than in the Euclidean norm. We give two new oblivious dimensionality reduction tech…

Active Linear Regression for ℓp​ Norms and Beyond

Improved Algorithms for Low Rank Approximation from Sparsity

Exponentially Improved Dimensionality Reduction for ℓ1​: Subspace Embeddings and Independence Testing

Active Linear Regression for $ℓ_{p}$ Norms and Beyond

Exponentially Improved Dimensionality Reduction for $ℓ_{1}$ : Subspace Embeddings and Independence Testing