| 2025 | FOCS | Root Ridge Leverage Score Sampling for ℓp Subspace Approximation. | David P. Woodruff, Taisuke Yasuda |
| 2025 | ICLR | Streaming Algorithms For ℓp Flows and ℓp Regression. | Amit Chakrabarti, Jeffrey Jiang, David P. Woodruff, Taisuke Yasuda |
| 2024 | ICML | Reweighted Solutions for Weighted Low Rank Approximation. | David P. Woodruff, Taisuke Yasuda |
| 2024 | ICML | Coresets for Multiple ℓp Regression. | David P. Woodruff, Taisuke Yasuda |
| 2023 | ICLR | Sequential Attention for Feature Selection. | Taisuke Yasuda, Mohammad Hossein Bateni, Lin Chen, Matthew Fahrbach, Gang Fu, Vahab Mirrokni |
| 2023 | ICML | Sharper Bounds for ℓ | David P. Woodruff, Taisuke Yasuda |
| 2023 | SODA | Online Lewis Weight Sampling. | David P. Woodruff, Taisuke Yasuda |
| 2023 | STOC | New Subset Selection Algorithms for Low Rank Approximation: Offline and Online. | David P. Woodruff, Taisuke Yasuda |
| 2022 | FOCS | Active Linear Regression for ℓp Norms and Beyond. | Cameron Musco, Christopher Musco, David P. Woodruff, Taisuke Yasuda |
| 2022 | FOCS | High-Dimensional Geometric Streaming in Polynomial Space. | David P. Woodruff, Taisuke Yasuda |
| 2022 | SODA | Improved Algorithms for Low Rank Approximation from Sparsity. | David P. Woodruff, Taisuke Yasuda |
| 2021 | COLT | Exponentially Improved Dimensionality Reduction for l1: Subspace Embeddings and Independence Testing. | Yi Li, David P. Woodruff, Taisuke Yasuda |
| 2019 | ICML | Tight Kernel Query Complexity of Kernel Ridge Regression and Kernel $k$-means Clustering. | Taisuke Yasuda, David P. Woodruff, Manuel Fernandez |