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Yuki Takezawa
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2020 – today
- 2024
- [j4]Iifan Tyou
, Tomoya Murata
A Localized Primal-Dual Method for Centralized/Decentralized Federated Learning Robust to Data Heterogeneity. IEEE Trans. Signal Inf. Process. over Networks 10: 94-107 (2024) - [i13]Satoki Ishikawa, Makoto Yamada, Han Bao, Yuki Takezawa:
PhiNets: Brain-inspired Non-contrastive Learning Based on Temporal Prediction Hypothesis. CoRR abs/2405.14650 (2024) - [i12]Yuki Takezawa, Han Bao, Ryoma Sato, Kenta Niwa, Makoto Yamada:
Polyak Meets Parameter-free Clipped Gradient Descent. CoRR abs/2405.15010 (2024) - 2023
- [j3]Yuki Takezawa, Han Bao, Kenta Niwa, Ryoma Sato, Makoto Yamada:
Momentum Tracking: Momentum Acceleration for Decentralized Deep Learning on Heterogeneous Data. Trans. Mach. Learn. Res. 2023 (2023) - [j2]Yuki Takezawa
Communication Compression for Decentralized Learning With Operator Splitting Methods. IEEE Trans. Signal Inf. Process. over Networks 9: 581-595 (2023) - [c5]Cléa Laouar, Yuki Takezawa, Makoto Yamada:
Large-scale similarity search with Optimal Transport. EMNLP 2023: 11920-11930 - [c4]Kazutoshi Shinoda, Yuki Takezawa
Improving the Robustness to Variations of Objects and Instructions with a Neuro-Symbolic Approach for Interactive Instruction Following. MMM (2) 2023: 635-646 - [c3]Yuki Takezawa, Ryoma Sato, Han Bao, Kenta Niwa, Makoto Yamada:
Beyond Exponential Graph: Communication-Efficient Topologies for Decentralized Learning via Finite-time Convergence. NeurIPS 2023 - [i11]Yuki Takezawa, Ryoma Sato, Han Bao, Kenta Niwa, Makoto Yamada:
Beyond Exponential Graph: Communication-Efficient Topologies for Decentralized Learning via Finite-time Convergence. CoRR abs/2305.11420 (2023) - [i10]Yuki Takezawa, Ryoma Sato, Han Bao, Kenta Niwa, Makoto Yamada:
Necessary and Sufficient Watermark for Large Language Models. CoRR abs/2310.00833 (2023) - [i9]Ryoma Sato, Yuki Takezawa, Han Bao, Kenta Niwa, Makoto Yamada:
Embarrassingly Simple Text Watermarks. CoRR abs/2310.08920 (2023) - [i8]Makoto Yamada, Yuki Takezawa, Guillaume Houry, Kira Michaela Dusterwald, Déborah Sulem, Han Zhao, Yao-Hung Hubert Tsai:
An Empirical Study of Simplicial Representation Learning with Wasserstein Distance. CoRR abs/2310.10143 (2023) - 2022
- [j1]Makoto Yamada, Yuki Takezawa, Ryoma Sato, Han Bao, Zornitsa Kozareva, Sujith Ravi:
Approximating 1-Wasserstein Distance with Trees. Trans. Mach. Learn. Res. 2022 (2022) - [c2]Yuki Takezawa
Fixed Support Tree-Sliced Wasserstein Barycenter. AISTATS 2022: 1120-1137 - [i7]Yuki Takezawa, Kenta Niwa, Makoto Yamada:
Communication Compression for Decentralized Learning with Operator Splitting Methods. CoRR abs/2205.03779 (2022) - [i6]Yuki Takezawa, Kenta Niwa, Makoto Yamada:
Theoretical Analysis of Primal-Dual Algorithm for Non-Convex Stochastic Decentralized Optimization. CoRR abs/2205.11979 (2022) - [i5]Makoto Yamada
Approximating 1-Wasserstein Distance with Trees. CoRR abs/2206.12116 (2022) - [i4]Yuki Takezawa, Han Bao, Kenta Niwa, Ryoma Sato, Makoto Yamada
Momentum Tracking: Momentum Acceleration for Decentralized Deep Learning on Heterogeneous Data. CoRR abs/2209.15505 (2022) - 2021
- [c1]Yuki Takezawa, Ryoma Sato, Makoto Yamada:
Supervised Tree-Wasserstein Distance. ICML 2021: 10086-10095 - [i3]Yuki Takezawa, Ryoma Sato, Makoto Yamada:
Supervised Tree-Wasserstein Distance. CoRR abs/2101.11520 (2021) - [i2]Yuki Takezawa, Ryoma Sato, Zornitsa Kozareva, Sujith Ravi, Makoto Yamada:
Fixed Support Tree-Sliced Wasserstein Barycenter. CoRR abs/2109.03431 (2021) - [i1]Kazutoshi Shinoda, Yuki Takezawa, Masahiro Suzuki, Yusuke Iwasawa, Yutaka Matsuo:
Improving the Robustness to Variations of Objects and Instructions with a Neuro-Symbolic Approach for Interactive Instruction Following. CoRR abs/2110.07031 (2021)
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