3 citations · 3 across the 1 of their papers we have counts for
5 papers
Enhancing the Interpretability of SHAP Values Using Large Language Models
Xianlong Zeng, Kewen Zhu
Model interpretability is crucial for understanding and trusting the decisions made by complex machine learning models, such as those built with XGBoost. SHAP (SHapley Additive exP…
CARE: Compatibility-Aware Incentive Mechanisms for Federated Learning with Budgeted Requesters
Xiang Liu, Hau Chan, Minming Li +3
Federated learning (FL) is a promising approach that allows requesters (\eg, servers) to obtain local training models from workers (e.g., clients). Since workers are typically unwi…
Similar Data Points Identification with LLM: A Human-in-the-loop Strategy Using Summarization and Hidden State Insights
Xianlong Zeng, Yijing Gao, Fanghao Song +1
This study introduces a simple yet effective method for identifying similar data points across non-free text domains, such as tabular and image data, using Large Language Models (L…
Translating Expert Intuition into Quantifiable Features: Encode Investigator Domain Knowledge via LLM for Enhanced Predictive Analytics
Phoebe Jing, Yijing Gao, Yuanhang Zhang +1
In the realm of predictive analytics, the nuanced domain knowledge of investigators often remains underutilized, confined largely to subjective interpretations and ad hoc decision-…
A Customer Level Fraudulent Activity Detection Benchmark for Enhancing Machine Learning Model Research and Evaluation
Phoebe Jing, Yijing Gao, Xianlong Zeng
In the field of fraud detection, the availability of comprehensive and privacy-compliant datasets is crucial for advancing machine learning research and developing effective anti-f…