1 citations · 2 across the 7 of their papers we have counts for
8 papers
Long-Context Demonstration Selection Using State Space Models
Ziniu Zhang, Zhenshuo Zhang, Ruoxuan Xiong +2
We study the problem of demonstration selection, which involves selecting a subset of examples for prepending to a query to a language model. This problem is closely related to in-…
Two-Sided State-Space Models for Sequential Recommendation with Non-Random Multimodal Review Feedback
Ziwen Pan, Zihan Liang, Ruoxuan Xiong
Two-sided digital platforms are inherently dynamic: user preferences shift, item popularity evolves, and reviews both reflect and drive these changes. Yet most sequential recommend…
Learning Dynamic Representations and Policies from Multimodal Clinical Time-Series with Informative Missingness
Zihan Liang, Ziwen Pan, Ruoxuan Xiong
Multimodal clinical records contain structured measurements and clinical notes recorded over time, offering rich temporal information about the evolution of patient health. Yet the…
AI-Generated Measurements for Identification and Inference with Missing Data: A Weak Shadow Variable Approach
Hongyu Chen, David Simchi-Levi, Ruoxuan Xiong
Across business and social science applications, outcomes are often missing in ways that depend on the unobserved outcomes themselves. In service systems, for example, whether a cu…
Causal Representation Learning from Multimodal Clinical Records under Non-Random Modality Missingness
Zihan Liang, Ziwen Pan, Ruoxuan Xiong
Clinical notes contain rich patient information, such as diagnoses or medications, making them valuable for patient representation learning. Recent advances in large language model…
Can We Validate Counterfactual Estimations in the Presence of General Network Interference?
Sadegh Shirani, Yuwei Luo, William Overman +2
Randomized experiments have become a cornerstone of evidence-based decision-making in contexts ranging from online platforms to public health. However, in experimental settings wit…
Higher-Order Causal Message Passing for Experimentation with Complex Interference
Mohsen Bayati, Yuwei Luo, William Overman +2
Accurate estimation of treatment effects is essential for decision-making across various scientific fields. This task, however, becomes challenging in areas like social sciences an…
A tractable ellipsoidal approximation for voltage regulation problems
Pan Li, Baihong Jin, Ruoxuan Xiong +3
We present a machine learning approach to the solution of chance constrained optimizations in the context of voltage regulation problems in power system operation. The novelty of o…