4 papers
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…