5 papers
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…
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…
Generative Flows for Potential Outcomes and Counterfactuals
Dongze Wu, David I. Inouye, Yao Xie
Average treatment effects summarize how interventions change outcomes, but individualized decisions require predictions and outcome distributions of what would have happened to the…
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…
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.…