10 citations · 10 across the 6 of their papers we have counts for
7 papers
BrainTRACE: Tracing Longitudinal, Multimodal, and Volumetric Evidence in Brain MRI Clinical Reasoning
Qizhen Lan, Mengchen Fan, Hang Zhang +5
Brain MRI interpretation is a longitudinal clinical reasoning problem: radiologists compare serial studies, integrate information across MRI sequences, localize findings within vol…
CRAD: Class-wise Reliability-Aware Distillation for Decentralized Heterogeneous Federated Learning
Baraa Bilbeisi, Mengchen Fan, Baocheng Geng +1
Conventional federated learning (FL) relies on parameter averaging, which forces clients to be doubly homogeneous: it demands an identical architecture and degrades under non-IID d…
Trust Is Not Enough: Influence Calibration for On-Policy Self-Distillation in Agentic RL
Qizhen Lan, Xi Xiao, Xiangchen Guan +4
On-policy self-distillation (OPSD) gives language agents dense token-level supervision from a privileged self-teacher on the policy's own trajectories. Existing methods allocate th…
Detail Consistent Stage-Wise Distillation for Efficient 3D MRI Segmentation
Mengchen Fan, Baocheng Geng, Xi Xiao +5
Deploying high-performing 3D medical image segmenters (e.g., nnU-Net) is often limited by memory footprint and inference latency. Compression is therefore necessary, but compact 3D…
PFedDST: Personalized Federated Learning with Decentralized Selection Training
Mengchen Fan, Keren Li, Tianyun Zhang +2
Distributed Learning (DL) enables the training of machine learning models across multiple devices, yet it faces challenges like non-IID data distributions and device capability dis…
Measuring Heterogeneity in Machine Learning with Distributed Energy Distance
Mengchen Fan, Baocheng Geng, Roman Shterenberg +3
In distributed and federated learning, heterogeneity across data sources remains a major obstacle to effective model aggregation and convergence. We focus on feature heterogeneity…
Interpretable Data Fusion for Distributed Learning: A Representative Approach via Gradient Matching
Mengchen Fan, Baocheng Geng, Keren Li +2
This paper introduces a representative-based approach for distributed learning that transforms multiple raw data points into a virtual representation. Unlike traditional distribute…