1 citations · 1 across the 10 of their papers we have counts for
11 papers
Learning a Size-Weight Frontier for Synthetic-Augmented Inference
Chengpiao Huang, Kaizheng Wang
Synthetic data can improve statistical inference when real data are scarce, but naively treating synthetic samples as real data can introduce bias and lead to unreliable inference.…
Congruence Decomposition with Neural Block Solvers for Large-Scale PCI Assignment
Yeqing Qiu, Chengpiao Huang, Ye Xue +7
Physical Cell Identity (PCI) assignment is essential for interference management in dense 5G networks. As cellular networks scale, PCI reuse becomes unavoidable, which may cause co…
LLM-Powered Virtual Population for Demand Simulation and Pricing
Chengpiao Huang, Kaizheng Wang
We develop an LLM-powered virtual population model that simulates demand for pricing decisions, in settings where products are described by rich unstructured information, such as t…
SYN-DIGITS: A Synthetic Control Framework for Calibrated Digital Twin Simulation
Grace Jiarui Fan, Chengpiao Huang, Tianyi Peng +2
Persona simulation based on large language models---often referred to as digital twin simulation---is increasingly used for market research, recommender systems, and social science…
The Nonstationarity-Complexity Tradeoff in Return Prediction
Agostino Capponi, Chengpiao Huang, J. Antonio Sidaoui +2
Does more data improve return prediction? In non-stationary financial markets, longer training windows improve prediction of complex models but incorporate outdated economic regime…
Relaxation-Free Min-k-Partition for PCI Assignment in 5G Networks
Yeqing Qiu, Chengpiao Huang, Ye Xue +4
Physical Cell Identity (PCI) is a critical parameter in 5G networks. Efficient and accurate PCI assignment is essential for mitigating mod-3 interference, mod-30 interference, coll…
How Many Human Survey Respondents is a Large Language Model Worth? An Uncertainty Quantification Perspective
Chengpiao Huang, Yuhang Wu, Kaizheng Wang
Large language models (LLMs) are increasingly used to simulate survey responses, but synthetic data can be misaligned with the human population, leading to unreliable inference. We…
A Similarity Measure Between Functions with Applications to Statistical Learning and Optimization
Chengpiao Huang, Kaizheng Wang
In this note, we present a novel measure of similarity between two functions. It quantifies how the sub-optimality gaps of two functions convert to each other, and unifies several…
Distribution-Free Predictive Inference under Unknown Temporal Drift
Elise Han, Chengpiao Huang, Kaizheng Wang
Distribution-free prediction sets play a pivotal role in uncertainty quantification for complex statistical models. Their validity hinges on reliable calibration data, which may no…
Model Assessment and Selection under Temporal Distribution Shift
Elise Han, Chengpiao Huang, Kaizheng Wang
We investigate model assessment and selection in a changing environment, by synthesizing datasets from both the current time period and historical epochs. To tackle unknown and pot…
A Stability Principle for Learning under Non-Stationarity
Chengpiao Huang, Kaizheng Wang
We develop a versatile framework for statistical learning in non-stationary environments. In each time period, our approach applies a stability principle to select a look-back wind…