4 papers
Neural Bayesian Sequential Routing
Yongchao Huang
Human decision-making is sequential and uncertainty-aware, yet standard neural networks often rely on static, dense forward computation with limited visibility into evidence acquis…
Gaussian Joint Embeddings For Self-Supervised Representation Learning
Yongchao Huang
Self-supervised representation learning often relies on deterministic predictive architectures to align context and target views in latent space. While effective in many settings,…
LLM-BI: Towards Fully Automated Bayesian Inference with Large Language Models
Yongchao Huang
A significant barrier to the widespread adoption of Bayesian inference is the specification of prior distributions and likelihoods, which often requires specialized statistical exp…
LLM-Prior: A Framework for Knowledge-Driven Prior Elicitation and Aggregation
Yongchao Huang
The specification of prior distributions is fundamental in Bayesian inference, yet it remains a significant bottleneck. The prior elicitation process is often a manual, subjective,…