1 citations · 1 across the 13 of their papers we have counts for
14 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.…
Tuning-Free Covariance-Aware Sequential Shrinkage for Multi-Source Estimation
Wenbo Jing, Xi Chen, Yaqi Duan +2
Modern statistical learning problems often involve multiple related data sets, in which learning efficiency on a target set can be improved by borrowing information from related so…
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…
Adaptive Querying with AI Persona Priors
Kaizheng Wang, Yuhang Wu, Assaf Zeevi
We study adaptive querying for learning user-dependent quantities of interest, such as responses to held-out items and psychometric indicators, within tight query budgets. Classica…
Estimating Continuous Treatment Effects with Two-Stage Kernel Ridge Regression
Seok-Jin Kim, Kaizheng Wang
We study the problem of estimating the effect function for a continuous treatment, which maps each treatment value to a population-averaged outcome. A central challenge in this set…
SYN-DIGITS: A Synthetic Control Framework for Calibrated Digital Twin Simulation
Grace Jiarui Fan, Chengpiao Huang, Tianyi Peng +2
AI-based persona simulation -- often referred to as digital twin simulation -- is increasingly used for market research, recommender systems, and social sciences. Despite their fle…
Pseudo-Labeling for Unsupervised Domain Adaptation with Kernel GLMs
Nathan Weill, Kaizheng Wang
We propose a principled framework for unsupervised domain adaptation under covariate shift in kernel Generalized Linear Models (GLMs), encompassing kernelized linear, logistic, and…
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 In non-stationary financial markets, longer training windows improve prediction of complex models but incorporate outdated economic regime…
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…
Adaptive Data Fusion for Multi-task Non-smooth Optimization
Henry Lam, Kaizheng Wang, Yuhang Wu +1
We study the problem of multi-task non-smooth optimization that arises ubiquitously in statistical learning, decision-making and risk management. We develop a data fusion approach…