Taisuke Yasuda

Orcid: 0000-0002-7003-9934

Affiliations:
  • Carnegie Mellon University, USA


According to our database1, Taisuke Yasuda authored at least 20 papers between 2019 and 2024.

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Bibliography

2024
Nearly Linear Sparsification of ℓ<sub>p</sub> Subspace Approximation.
CoRR, 2024

Coresets for Multiple ℓ<sub>p</sub> Regression.
CoRR, 2024

SequentialAttention++ for Block Sparsification: Differentiable Pruning Meets Combinatorial Optimization.
CoRR, 2024

Coresets for Multiple ℓp Regression.
Proceedings of the Forty-first International Conference on Machine Learning, 2024

Reweighted Solutions for Weighted Low Rank Approximation.
Proceedings of the Forty-first International Conference on Machine Learning, 2024

2023
Performance of 𝓁<sub>1</sub> Regularization for Sparse Convex Optimization.
CoRR, 2023

New Subset Selection Algorithms for Low Rank Approximation: Offline and Online.
Proceedings of the 55th Annual ACM Symposium on Theory of Computing, 2023

Online Lewis Weight Sampling.
Proceedings of the 2023 ACM-SIAM Symposium on Discrete Algorithms, 2023

Sketching Algorithms for Sparse Dictionary Learning: PTAS and Turnstile Streaming.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Sharper Bounds for ℓ<sub>p</sub> Sensitivity Sampling.
Proceedings of the International Conference on Machine Learning, 2023

Sequential Attention for Feature Selection.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022
Improved Algorithms for Low Rank Approximation from Sparsity.
Proceedings of the 2022 ACM-SIAM Symposium on Discrete Algorithms, 2022

High-Dimensional Geometric Streaming in Polynomial Space.
Proceedings of the 63rd IEEE Annual Symposium on Foundations of Computer Science, 2022

Active Linear Regression for ℓp Norms and Beyond.
Proceedings of the 63rd IEEE Annual Symposium on Foundations of Computer Science, 2022

2021
Active Sampling for Linear Regression Beyond the $\ell_2$ Norm.
CoRR, 2021

Exponentially Improved Dimensionality Reduction for 𝓁<sub>1</sub>: Subspace Embeddings and Independence Testing.
CoRR, 2021

Exponentially Improved Dimensionality Reduction for l1: Subspace Embeddings and Independence Testing.
Proceedings of the Conference on Learning Theory, 2021

2020
Graph Spanners in the Message-Passing Model.
Proceedings of the 11th Innovations in Theoretical Computer Science Conference, 2020

2019
Tight Kernel Query Complexity of Kernel Ridge Regression and Kernel $k$-means Clustering.
Proceedings of the 36th International Conference on Machine Learning, 2019

The Query Complexity of Mastermind with l<sub>p</sub> Distances.
Proceedings of the Approximation, 2019


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