Yucheng Lu

MixML: A Unified Analysis of Weakly Consistent Parallel Learning

2020 · arXiv (Cornell University)

Parallelism is a ubiquitous method for accelerating machine learning algorithms. However, theoretical analysis of parallel learning is usually done in an algorithm- and protocol-specific setting, giving little insight about how changes in the structure of …

Feeding LLM Annotations to BERT Classifiers at Your Own Risk

2025 · arXiv (Cornell University)

Using LLM-generated labels to fine-tune smaller encoder-only models for text classification has gained popularity in various settings. While this approach may be justified in simple and low-stakes applications, we conduct empirical analysis to demonstrate how …

Intuitions of Machine Learning Researchers about Transfer Learning for Medical Image Classification

2025 · arXiv (Cornell University)

Transfer learning is crucial for medical imaging, yet the selection of source datasets often relies on researchers' intuition rather than systematic principles, which can impact the generalizability of algorithms and, thus, patient outcomes. This study …