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stat.ML2026
Synthetic Oversampling: Theory and A Practical Approach Using LLMs to Address Data Imbalance
Ryumei Nakada, Yichen Xu, Lexin Li +1
Imbalanced classification and spurious correlation are common challenges in data science and machine learning. Both issues are linked to data imbalance, with certain groups of data…
stat.ML2025
A Statistical Hypothesis Testing Framework for Data Misappropriation Detection in Large Language Models
Yinpeng Cai, Lexin Li, Linjun Zhang
Large Language Models (LLMs) are rapidly gaining enormous popularity in recent years. However, the training of LLMs has raised significant privacy and legal concerns, particularly…
stat.ML2025
Contrastive Network Representation Learning
Zihan Dong, Xin Zhou, Ryumei Nakada +2
Network representation learning seeks to embed networks into a low-dimensional space while preserving the structural and semantic properties, thereby facilitating downstream tasks…