Yuki Takezawa

Orcid: 0000-0002-8532-2775

According to our database1, Yuki Takezawa authored at least 15 papers between 2021 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

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PhD thesis 
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Links

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Bibliography

2024
A Localized Primal-Dual Method for Centralized/Decentralized Federated Learning Robust to Data Heterogeneity.
IEEE Trans. Signal Inf. Process. over Networks, 2024

Polyak Meets Parameter-free Clipped Gradient Descent.
CoRR, 2024

PhiNets: Brain-inspired Non-contrastive Learning Based on Temporal Prediction Hypothesis.
CoRR, 2024

2023
Communication Compression for Decentralized Learning With Operator Splitting Methods.
IEEE Trans. Signal Inf. Process. over Networks, 2023

Momentum Tracking: Momentum Acceleration for Decentralized Deep Learning on Heterogeneous Data.
Trans. Mach. Learn. Res., 2023

An Empirical Study of Simplicial Representation Learning with Wasserstein Distance.
CoRR, 2023

Embarrassingly Simple Text Watermarks.
CoRR, 2023

Necessary and Sufficient Watermark for Large Language Models.
CoRR, 2023

Beyond Exponential Graph: Communication-Efficient Topologies for Decentralized Learning via Finite-time Convergence.
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.
Proceedings of the MultiMedia Modeling - 29th International Conference, 2023

Large-scale similarity search with Optimal Transport.
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, 2023

2022
Approximating 1-Wasserstein Distance with Trees.
Trans. Mach. Learn. Res., 2022

Theoretical Analysis of Primal-Dual Algorithm for Non-Convex Stochastic Decentralized Optimization.
CoRR, 2022

Fixed Support Tree-Sliced Wasserstein Barycenter.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021
Supervised Tree-Wasserstein Distance.
Proceedings of the 38th International Conference on Machine Learning, 2021


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