5 papers
Generative models for decision-making under distributional shift
Xiuyuan Cheng, Yunqin Zhu, Yao Xie
Many data-driven decision problems are formulated using a nominal distribution estimated from historical data, while performance is ultimately determined by a deployment distributi…
CoreFlow: Low-Rank Matrix Generative Models
Dongze Wu, Linglingzhi Zhu, Yao Xie
Learning matrix-valued distributions from high-dimensional and possibly incomplete training data is challenging: ambient-space generative modeling is computationally expensive and…
Flow-based Generative Modeling of Potential Outcomes and Counterfactuals
Dongze Wu, David I. Inouye, Yao Xie
Predicting potential and counterfactual outcomes from observational data is central to individualized decision-making, particularly in clinical settings where treatment choices mus…
Annealing Flow Generative Models Towards Sampling High-Dimensional and Multi-Modal Distributions
Dongze Wu, Yao Xie
Sampling from high-dimensional, multi-modal distributions remains a fundamental challenge across domains such as statistical Bayesian inference and physics-based machine learning.…
Graph-Based Prediction Models for Data Debiasing
Dongze Wu, Hanyang Jiang, Yao Xie
Bias in data collection, arising from both under-reporting and over-reporting, poses significant challenges in critical applications such as healthcare and public safety. In this w…