Supervised Tree-Wasserstein Distance. - Yuki Takezawa; Ryoma Sato; Makoto Yamada (ICML, 2021)

Fixed Support Tree-Sliced Wasserstein Barycenter. - Yuki Takezawa; Ryoma Sato; Zornitsa Kozareva; Sujith Ravi; Makoto Yamada (AISTATS, 2022)

Theoretical Analysis of Primal-Dual Algorithm for Non-Convex Stochastic Decentralized Optimization. - Yuki Takezawa; Kenta Niwa; Makoto Yamada (CoRR, 2022)

Approximating 1-Wasserstein Distance with Trees. - Makoto Yamada; Yuki Takezawa; Ryoma Sato; Han Bao 002; Zornitsa Kozareva; Sujith Ravi (Trans. Mach. Learn. Res., 2022)

Large-scale similarity search with Optimal Transport. - Cléa Laouar; Yuki Takezawa; Makoto Yamada (EMNLP, 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 001; Yusuke Iwasawa; Yutaka Matsuo (MMM (2), 2023)

Beyond Exponential Graph: Communication-Efficient Topologies for Decentralized Learning via Finite-time Convergence. - Yuki Takezawa; Ryoma Sato; Han Bao 002; Kenta Niwa; Makoto Yamada (NeurIPS, 2023)

Embarrassingly Simple Text Watermarks. - Ryoma Sato; Yuki Takezawa; Han Bao 002; Kenta Niwa; Makoto Yamada (CoRR, 2023)

An Empirical Study of Simplicial Representation Learning with Wasserstein Distance. - Makoto Yamada; Yuki Takezawa; Guillaume Houry; Kira Michaela Dusterwald; Déborah Sulem; Han Zhao 002; Yao-Hung Hubert Tsai (CoRR, 2023)

Momentum Tracking: Momentum Acceleration for Decentralized Deep Learning on Heterogeneous Data. - Yuki Takezawa; Han Bao 002; Kenta Niwa; Ryoma Sato; Makoto Yamada (Trans. Mach. Learn. Res., 2023)

Communication Compression for Decentralized Learning With Operator Splitting Methods. - Yuki Takezawa; Kenta Niwa; Makoto Yamada (IEEE Trans. Signal Inf. Process. over Networks, 2023)

Parameter-free Clipped Gradient Descent Meets Polyak. - Yuki Takezawa; Han Bao 002; Ryoma Sato; Kenta Niwa; Makoto Yamada (NeurIPS, 2024)

Polyak Meets Parameter-free Clipped Gradient Descent. - Yuki Takezawa; Han Bao 002; Ryoma Sato; Kenta Niwa; Makoto Yamada (CoRR, 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)

PhiNets: Brain-inspired Non-contrastive Learning Based on Temporal Prediction Hypothesis. - Satoki Ishikawa; Makoto Yamada; Han Bao 002; Yuki Takezawa (ICLR, 2025)

Scalable Decentralized Learning with Teleportation. - Yuki Takezawa; Sebastian U. Stich (ICLR, 2025)

Necessary and Sufficient Watermark for Large Language Models. - Yuki Takezawa; Ryoma Sato; Han Bao 002; Kenta Niwa; Makoto Yamada (Trans. Mach. Learn. Res., 2025)

Any-stepsize Gradient Descent for Separable Data under Fenchel-Young Losses. - Han Bao 002; Shinsaku Sakaue; Yuki Takezawa (CoRR, 2025)

Exploiting Similarity for Computation and Communication-Efficient Decentralized Optimization. - Yuki Takezawa; Xiaowen Jiang; Anton Rodomanov; Sebastian U. Stich (CoRR, 2025)