1 citations · 1 across the 4 of their papers we have counts for
5 papers
Merge and Branch: Efficient Tree-Structured Supervised Fine-Tuning of LLMs for Recommendation
Yunqin Zhu, Yao Xie, Liyang Shu +4
LLM-based generative recommendation reads long, text-rich interaction histories but generates only short semantic identifiers (SIDs). This asymmetry makes supervised fine-tuning in…
OutageDiT: A Foundation Model for Power Outage Forecasting and Scenario Generation
Yunqin Zhu, Feng Qiu, Yao Xie
Power-outage planning requires scenarios before an event occurs. These scenarios must represent uncertainty in magnitude, timing, and duration while preserving temporal dependence.…
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…
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…
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…