Short-Term Precipitation Prediction with Skip-Connected PredNet. - Ryoma Sato; Hisashi Kashima; Takehiro Yamamoto (ICANN (3), 2018)

Learning to Sample Hard Instances for Graph Algorithms. - Ryoma Sato; Makoto Yamada; Hisashi Kashima (ACML, 2019)

Approximation Ratios of Graph Neural Networks for Combinatorial Problems. - Ryoma Sato; Makoto Yamada; Hisashi Kashima (NeurIPS, 2019)

Learning to Find Hard Instances of Graph Problems. - Ryoma Sato; Makoto Yamada; Hisashi Kashima (CoRR, 2019)

Fast Unbalanced Optimal Transport on a Tree. - Ryoma Sato; Makoto Yamada; Hisashi Kashima (NeurIPS, 2020)

Fast and Robust Comparison of Probability Measures in Heterogeneous Spaces. - Ryoma Sato; Marco Cuturi; Makoto Yamada; Hisashi Kashima (CoRR, 2020)

A Survey on The Expressive Power of Graph Neural Networks. - Ryoma Sato (CoRR, 2020)

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

Random Features Strengthen Graph Neural Networks. - Ryoma Sato; Makoto Yamada; Hisashi Kashima (SDM, 2021)

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

Towards Principled User-side Recommender Systems. - Ryoma Sato (CIKM, 2022)

CLEAR: A Fully User-side Image Search System. - Ryoma Sato (CIKM, 2022)

Twin Papers: A Simple Framework of Causal Inference for Citations via Coupling. - Ryoma Sato; Makoto Yamada; Hisashi Kashima (CIKM, 2022)

Re-evaluating Word Mover's Distance. - Ryoma Sato; Makoto Yamada; Hisashi Kashima (ICML, 2022)

Word Tour: One-dimensional Word Embeddings via the Traveling Salesman Problem. - Ryoma Sato (NAACL-HLT, 2022)

Feature-Robust Optimal Transport for High-Dimensional Data. - Mathis Petrovich; Chao Liang 002; Ryoma Sato; Yanbin Liu; Yao-Hung Hubert Tsai; Linchao Zhu; Yi Yang 001; Ruslan Salakhutdinov; Makoto Yamada (ECML/PKDD (5), 2022)

Private Recommender Systems: How Can Users Build Their Own Fair Recommender Systems without Log Data? - Ryoma Sato (SDM, 2022)

Enumerating Fair Packages for Group Recommendations. - Ryoma Sato (WSDM, 2022)

Retrieving Black-box Optimal Images from External Databases. - Ryoma Sato (WSDM, 2022)

Poincare: Recommending Publication Venues via Treatment Effect Estimation. - Ryoma Sato; Makoto Yamada; Hisashi Kashima (J. Informetrics, 2022)

Constant Time Graph Neural Networks. - Ryoma Sato; Makoto Yamada; Hisashi Kashima (ACM Trans. Knowl. Discov. Data, 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)

Graph Neural Networks can Recover the Hidden Features Solely from the Graph Structure. - Ryoma Sato (ICML, 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)

Active Learning from the Web. - Ryoma Sato (WWW, 2023)

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

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

User-Side Realization. - Ryoma Sato (CoRR, 2024)

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

Overhead-free User-side Recommender Systems. - Ryoma Sato (CoRR, 2024)

Making Translators Privacy-aware on the User's Side. - Ryoma Sato (Trans. Mach. Learn. Res., 2024)

Training-free Graph Neural Networks and the Power of Labels as Features. - Ryoma Sato (Trans. Mach. Learn. Res., 2024)

Influential Bandits: Pulling an Arm May Change the Environment. - Ryoma Sato; Shinji Ito (Trans. Mach. Learn. Res., 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)

Solving the Cold Start Problem on One's Own as an End User via Preference Transfer. - Ryoma Sato (CoRR, 2025)