Yuki Takezawa

According to our database, Yuki Takezawa authored at least 19 papers between 2021 and 2025.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2025

Exploiting Similarity for Computation and Communication-Efficient Decentralized Optimization.

Yuki Takezawa, Xiaowen Jiang, Anton Rodomanov, Sebastian U. Stich

CoRR, June, 2025

Any-stepsize Gradient Descent for Separable Data under Fenchel-Young Losses.

Han Bao, Shinsaku Sakaue, Yuki Takezawa

CoRR, February, 2025

Necessary and Sufficient Watermark for Large Language Models.

Yuki Takezawa, Ryoma Sato, Han Bao, Kenta Niwa, Makoto Yamada

Trans. Mach. Learn. Res., 2025

Scalable Decentralized Learning with Teleportation.

Yuki Takezawa, Sebastian U. Stich

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

PhiNets: Brain-inspired Non-contrastive Learning Based on Temporal Prediction Hypothesis.

Satoki Ishikawa, Makoto Yamada, Han Bao, Yuki Takezawa

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

2024

A Localized Primal-Dual Method for Centralized/Decentralized Federated Learning Robust to Data Heterogeneity.

Iifan Tyou, Tomoya Murata, Takumi Fukami, Yuki Takezawa, Kenta Niwa

IEEE Trans. Signal Inf. Process. over Networks, 2024

Polyak Meets Parameter-free Clipped Gradient Descent.

Yuki Takezawa, Han Bao, Ryoma Sato, Kenta Niwa, Makoto Yamada

CoRR, 2024

Parameter-free Clipped Gradient Descent Meets Polyak.

Yuki Takezawa, Han Bao, Ryoma Sato, Kenta Niwa, Makoto Yamada

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

2023

Communication Compression for Decentralized Learning With Operator Splitting Methods.

Yuki Takezawa, Kenta Niwa, Makoto Yamada

IEEE Trans. Signal Inf. Process. over Networks, 2023

Momentum Tracking: Momentum Acceleration for Decentralized Deep Learning on Heterogeneous Data.

Yuki Takezawa, Han Bao, Kenta Niwa, Ryoma Sato, Makoto Yamada

Trans. Mach. Learn. Res., 2023

An Empirical Study of Simplicial Representation Learning with Wasserstein Distance.

Makoto Yamada, Yuki Takezawa, Guillaume Houry, Kira Michaela Dusterwald, Déborah Sulem, Han Zhao, Yao-Hung Hubert Tsai

CoRR, 2023

Embarrassingly Simple Text Watermarks.

Ryoma Sato, Yuki Takezawa, Han Bao, Kenta Niwa, Makoto Yamada

CoRR, 2023

Beyond Exponential Graph: Communication-Efficient Topologies for Decentralized Learning via Finite-time Convergence.

Yuki Takezawa, Ryoma Sato, Han Bao, Kenta Niwa, Makoto Yamada

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Improving the Robustness to Variations of Objects and Instructions with a Neuro-Symbolic Approach for Interactive Instruction Following.

Kazutoshi Shinoda, Yuki Takezawa, Masahiro Suzuki, Yusuke Iwasawa, Yutaka Matsuo

Proceedings of the MultiMedia Modeling - 29th International Conference, 2023

Large-scale similarity search with Optimal Transport.

Cléa Laouar, Yuki Takezawa, Makoto Yamada

Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, 2023

2022

Approximating 1-Wasserstein Distance with Trees.

Makoto Yamada, Yuki Takezawa, Ryoma Sato, Han Bao, Zornitsa Kozareva, Sujith Ravi

Trans. Mach. Learn. Res., 2022

Theoretical Analysis of Primal-Dual Algorithm for Non-Convex Stochastic Decentralized Optimization.

Yuki Takezawa, Kenta Niwa, Makoto Yamada

CoRR, 2022

Fixed Support Tree-Sliced Wasserstein Barycenter.

Yuki Takezawa, Ryoma Sato, Zornitsa Kozareva, Sujith Ravi, Makoto Yamada

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021

Supervised Tree-Wasserstein Distance.

Yuki Takezawa, Ryoma Sato, Makoto Yamada

Proceedings of the 38th International Conference on Machine Learning, 2021