1 citations · 1 across the 5 of their papers we have counts for
6 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…
DoFlow: Flow-based Generative Models for Interventional and Counterfactual Forecasting on Time Series
Dongze Wu, Feng Qiu, Yao Xie
Time-series forecasting increasingly demands not only accurate observational predictions but also causal forecasting under interventional and counterfactual queries in multivariate…
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.…
Self-supervised Learning-based Reconstruction of High-resolution 4D Light Fields
Jianxin Lei, Dongze Wu, Chengcai Xu +4
Hand-held light field (LF) cameras often exhibit low spatial resolution due to the inherent trade-off between spatial and angular dimensions. Existing supervised learning-based LF…