367 citations · 388 across the 5 of their papers we have counts for
5 papers
Memory Layer: Train the In-Model Cache for Recommendation Models
Liangyuan Na, Gufan Yin, Yixin Bao +19
Early ranking stages in recommendation systems precompute item embeddings and cache them in-model for scoring within strict latency constraints. Because this cache exists only at s…
Patient Outcome Predictions Improve Operations at a Large Hospital Network
Liangyuan Na, Kimberly Villalobos Carballo, Jean Pauphilet +8
Problem definition: Access to accurate predictions of patients' outcomes can enhance medical staff's decision-making, which ultimately benefits all stakeholders in the hospitals. A…
The Benefit of Uncertainty Coupling in Robust and Adaptive Robust Optimization
Dimitris Bertsimas, Liangyuan Na, Bartolomeo Stellato +1
Despite the modeling power for problems under uncertainty, robust optimization (RO) and adaptive robust optimization (ARO) can exhibit too conservative solutions in terms of object…
TabText: Language-Based Representations of Tabular Health Data for Predictive Modelling
Kimberly Villalobos Carballo, Liangyuan Na, Yu Ma +4
Tabular medical records remain the most readily available data format for applying machine learning in healthcare. However, traditional data preprocessing ignores valuable contextu…
Integrated multimodal artificial intelligence framework for healthcare applications
Luis R. Soenksen, Yu Ma, Cynthia Zeng +7
Artificial intelligence (AI) systems hold great promise to improve healthcare over the next decades. Specifically, AI systems leveraging multiple data sources and input modalities…