2 citations · 3 across the 3 of their papers we have counts for
3 papers
cs.LG2023★ 2 cited
When MiniBatch SGD Meets SplitFed Learning:Convergence Analysis and Performance Evaluation
Chao Huang, Geng Tian, Ming Tang
Federated learning (FL) enables collaborative model training across distributed clients (e.g., edge devices) without sharing raw data. Yet, FL can be computationally expensive as t…
cs.LG2023★ 1 cited
Feature Importance Measurement based on Decision Tree Sampling
Chao Huang, Diptesh Das, Koji Tsuda
Random forest is effective for prediction tasks but the randomness of tree generation hinders interpretability in feature importance analysis. To address this, we proposed DT-Sampl…
cs.RO2023
Realistic Safety-critical Scenarios Search for Autonomous Driving System via Behavior Tree
Ping Zhang, Lingfeng Ming, Tingyi Yuan +5
The simulation-based testing of Autonomous Driving Systems (ADSs) has gained significant attention. However, current approaches often fall short of accurately assessing ADSs for tw…