3 citations · 3 across the 10 of their papers we have counts for
5 papers · 1 filter
Can Tabular Foundation Models Amortize Statistical Inference?
Kai Ye, Shijin Gong, Hongyi Zhou +2
For decades, statistical inference has largely been developed one problem at a time. Given a scientific target, such as a treatment effect or a regression function, statisticians d…
Optimal Mixture-of-Experts Model Averaging for Conditional Generative Models
Shijin Gong, Baihua He, Xinyu Zhang
Conditional generative models have emerged as powerful tools for sampling from target conditional distributions, driving substantial advances across a wide range of scientific and…
PEARL: Performance-Enhanced Aggregated Representation Learning
Wenhui Li, Shijin Gong, Xinyu Zhang
Representation learning is a key technique in modern machine learning that enables models to identify meaningful patterns in complex data. However, different methods tend to extrac…
Deep Generative Demand Learning for Newsvendor and Pricing
Shijin Gong, Huihang Liu, Xinyu Zhang
We consider data-driven inventory and pricing decisions in the feature-based newsvendor problem, where demand is influenced by both price and contextual features and is modeled wit…
Towards Optimal Neural Networks: the Role of Sample Splitting in Hyperparameter Selection
Shijin Gong, Xinyu Zhang
When artificial neural networks have demonstrated exceptional practical success in a variety of domains, investigations into their theoretical characteristics, such as their approx…