3 papers
cs.LG2026
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…
stat.ML2026
Scalable Deep Basis Kernel Gaussian Processes
Yunqin Zhu, Henry Shaowu Yuchi, Yao Xie
Learning expressive kernels while retaining tractable inference remains a central challenge in scaling Gaussian processes (GPs) to large and complex datasets. We propose a scalable…
stat.ML2025
Worst-case generation via minimax optimization in Wasserstein space
Xiuyuan Cheng, Yao Xie, Linglingzhi Zhu +1
Worst-case generation plays a critical role in evaluating robustness and stress-testing systems under distribution shifts, in applications ranging from machine learning models to p…