A machine learning approach to model the received signal in molecular communications. - H. Birkan Yilmaz 001; Changmin Lee 002; Yae Jee Cho; Chan-Byoung Chae (BlackSeaCom, 2017)

Effective Enzyme Deployment for Degradation of Interference Molecules in Molecular Communication. - Yae Jee Cho; H. Birkan Yilmaz 001; Weisi Guo; Chan-Byoung Chae (WCNC, 2017)

Map-based Millimeter-Wave Channel Models: An Overview, Guidelines, and Data. - Yeon-Geun Lim; Yae Jee Cho; Younsun Kim; Chan-Byoung Chae (CoRR, 2017)

Effective inter-symbol interference mitigation with a limited amount of enzymes in molecular communications. - Yae Jee Cho; H. Birkan Yilmaz 001; Weisi Guo; Chan-Byoung Chae (Trans. Emerg. Telecommun. Technol., 2017)

V2X Downlink Coverage Analysis with a Realistic Urban Vehicular Model. - Yae Jee Cho; Kaibin Huang; Chan-Byoung Chae (GLOBECOM Workshops, 2018)

RF Lens-Embedded Antenna Array for mmWave MIMO: Design and Performance. - Yae Jee Cho; Gee-Yong Suk; Byoungnam Kim; Dong Ku Kim; Chan-Byoung Chae (IEEE Commun. Mag., 2018)

Relationship Between Cross-Polarization Discrimination (XPD) and Spatial Correlation in Indoor Small-Cell MIMO Systems. - Yeon-Geun Lim; Yae Jee Cho; TaeckKeun Oh; Yongshik Lee; Chan-Byoung Chae (IEEE Wirel. Commun. Lett., 2018)

Bandit-based Communication-Efficient Client Selection Strategies for Federated Learning. - Yae Jee Cho; Samarth Gupta; Gauri Joshi; Osman Yagan (ACSSC, 2020)

Client Selection in Federated Learning: Convergence Analysis and Power-of-Choice Selection Strategies. - Yae Jee Cho; Jianyu Wang; Gauri Joshi (CoRR, 2020)

Map-Based Millimeter-Wave Channel Models: An Overview, Data for B5G Evaluation and Machine Learning. - Yeon-Geun Lim; Yae Jee Cho; Min Soo Sim; Younsun Kim; Chan-Byoung Chae; Reinaldo A. Valenzuela (IEEE Wirel. Commun., 2020)

Personalized Federated Learning for Heterogeneous Clients with Clustered Knowledge Transfer. - Yae Jee Cho; Jianyu Wang; Tarun Chiruvolu; Gauri Joshi (CoRR, 2021)

Towards Understanding Biased Client Selection in Federated Learning. - Yae Jee Cho; Jianyu Wang; Gauri Joshi (AISTATS, 2022)

Heterogeneous Ensemble Knowledge Transfer for Training Large Models in Federated Learning. - Yae Jee Cho; Andre Manoel; Gauri Joshi; Robert Sim; Dimitrios Dimitriadis (IJCAI, 2022)

To Federate or Not To Federate: Incentivizing Client Participation in Federated Learning. - Yae Jee Cho; Divyansh Jhunjhunwala; Tian Li 005; Virginia Smith; Gauri Joshi (CoRR, 2022)

Local or Global: Selective Knowledge Assimilation for Federated Learning with Limited Labels. - Yae Jee Cho; Gauri Joshi; Dimitrios Dimitriadis (ICCV, 2023)

On the Convergence of Federated Averaging with Cyclic Client Participation. - Yae Jee Cho; Pranay Sharma; Gauri Joshi; Zheng Xu 002; Satyen Kale; Tong Zhang 001 (ICML, 2023)

Communication-Efficient and Model-Heterogeneous Personalized Federated Learning via Clustered Knowledge Transfer. - Yae Jee Cho; Jianyu Wang; Tarun Chirvolu; Gauri Joshi (IEEE J. Sel. Top. Signal Process., 2023)

Heterogeneous LoRA for Federated Fine-tuning of On-Device Foundation Models. - Yae Jee Cho; Luyang Liu; Zheng Xu 002; Aldi Fahrezi; Gauri Joshi (EMNLP, 2024)

Heterogeneous Low-Rank Approximation for Federated Fine-tuning of On-Device Foundation Models. - Yae Jee Cho; Luyang Liu; Zheng Xu 002; Aldi Fahrezi; Gauri Joshi (CoRR, 2024)

Maximizing Global Model Appeal in Federated Learning. - Yae Jee Cho; Divyansh Jhunjhunwala; Tian Li 005; Virginia Smith; Gauri Joshi (Trans. Mach. Learn. Res., 2024)