3 papers
cs.LG2026
Can Generative Artificial Intelligence Survive Data Contamination? Theoretical Guarantees under Contaminated Recursive Training
Kevin Wang, Hongqian Niu, Didong Li
As artificial intelligence (AI)-generated content proliferates, models are increasingly trained on their own outputs, risking progressive degradation or collapse. In this article,…
stat.AP2025
Incorporating LLM Embeddings for Variation Across the Human Genome
Hongqian Niu, Jordan Bryan, Jacob Williams +4
Recent advances in large language model (LLM) embeddings have enabled powerful representations for biological data, but most applications to date focus on gene-level information. W…
cs.LG2025
Deep Generative Models: Complexity, Dimensionality, and Approximation
Kevin Wang, Hongqian Niu, Yixin Wang +1
Generative networks have shown remarkable success in learning complex data distributions, particularly in generating high-dimensional data from lower-dimensional inputs. While this…