Yae Jee Cho
Orcid: 0000-0001-6075-2712
According to our database1,
Yae Jee Cho
authored at least 18 papers
between 2017 and 2024.
Collaborative distances:
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Bibliography
2024
Heterogeneous Low-Rank Approximation for Federated Fine-tuning of On-Device Foundation Models.
CoRR, 2024
2023
Communication-Efficient and Model-Heterogeneous Personalized Federated Learning via Clustered Knowledge Transfer.
IEEE J. Sel. Top. Signal Process., January, 2023
Proceedings of the International Conference on Machine Learning, 2023
Local or Global: Selective Knowledge Assimilation for Federated Learning with Limited Labels.
Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023
2022
To Federate or Not To Federate: Incentivizing Client Participation in Federated Learning.
CoRR, 2022
Heterogeneous Ensemble Knowledge Transfer for Training Large Models in Federated Learning.
Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, 2022
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022
2021
Personalized Federated Learning for Heterogeneous Clients with Clustered Knowledge Transfer.
CoRR, 2021
2020
Map-Based Millimeter-Wave Channel Models: An Overview, Data for B5G Evaluation and Machine Learning.
IEEE Wirel. Commun., 2020
Client Selection in Federated Learning: Convergence Analysis and Power-of-Choice Selection Strategies.
CoRR, 2020
Bandit-based Communication-Efficient Client Selection Strategies for Federated Learning.
Proceedings of the 54th Asilomar Conference on Signals, Systems, and Computers, 2020
2018
Relationship Between Cross-Polarization Discrimination (XPD) and Spatial Correlation in Indoor Small-Cell MIMO Systems.
IEEE Wirel. Commun. Lett., 2018
IEEE Commun. Mag., 2018
Proceedings of the IEEE Globecom Workshops, 2018
2017
Effective inter-symbol interference mitigation with a limited amount of enzymes in molecular communications.
Trans. Emerg. Telecommun. Technol., 2017
CoRR, 2017
Effective Enzyme Deployment for Degradation of Interference Molecules in Molecular Communication.
Proceedings of the 2017 IEEE Wireless Communications and Networking Conference, 2017
A machine learning approach to model the received signal in molecular communications.
Proceedings of the 2017 IEEE International Black Sea Conference on Communications and Networking, 2017