4 citations · 5 across the 4 of their papers we have counts for
7 papers
Model-Free Assessment of Simulator Fidelity via Quantile Curves
Garud Iyengar, Yu-Shiou Willy Lin, Kaizheng Wang
As generative AI models are increasingly used to simulate real-world systems, quantifying the ``sim-to-real'' gap is critical. For each input setting of interest -- which we call a…
A Minimalist Bayesian Framework for Stochastic Optimization
Kaizheng Wang
The Bayesian paradigm offers principled tools for sequential decision-making under uncertainty, but its reliance on a probabilistic model for all parameters can hinder the incorpor…
Transfer Learning of CATE with Kernel Ridge Regression
Seok-Jin Kim, Hongjie Liu, Molei Liu +1
The proliferation of data has sparked significant interest in leveraging findings from one study to estimate treatment effects in a different target population without direct outco…
A Particle Algorithm for Mean-Field Variational Inference
Qiang Du, Kaizheng Wang, Edith Zhang +1
Variational inference is a fast and scalable alternative to Markov chain Monte Carlo and has been widely applied to posterior inference tasks in statistics and machine learning. A…
Adaptive Transfer Clustering: A Unified Framework
Yuqi Gu, Zhongyuan Lyu, Kaizheng Wang
We propose a general transfer learning framework for clustering given a main dataset and an auxiliary one about the same subjects. The two datasets may reflect similar but differen…
Pseudo-Labeling for Kernel Ridge Regression under Covariate Shift
Kaizheng Wang
We develop and analyze a principled approach to kernel ridge regression under covariate shift. The goal is to learn a regression function with small mean squared error over a targe…
Variable Clustering via Distributionally Robust Nodewise Regression
Kaizheng Wang, Xiao Xu, Xun Yu Zhou
We study a multi-factor block model for variable clustering and connect it to regularized subspace clustering through a distributionally robust version of nodewise regression. To s…