13 papers
HSCI: Neyman-Orthogonal Causal Inference under High-Dimensional Proportional Hazards
Yingying Fan, Lan Gao, Daoji Li +1
Valid treatment effect inference in survival studies is fundamental yet challenging when the treatment assignments and outcomes are confounded by many baseline covariates. To this…
FrontierOR: Benchmarking LLMs' Capacity for Efficient Algorithm Design in Large-Scale Optimization
Minwei Kong, Chonghe Jiang, Ao Qu +24
Large language models (LLMs) are increasingly used for optimization modeling and solver-code generation, yet practical operations research and optimization problems often require a…
CART Random Forests as Sequential Allocation over Random Opportunity Sets: A Stochastic-Control Theory of Ensemble Risk
Tianxing Mei, Yingying Fan, Mingming Leng +1
CART random forests are among the most widely used modern predictive methods, with well-documented empirical success. Yet, at the mechanistic level, the algorithm is often treated…
Harnessing Unimodality in Semiparametric Contextual Pricing via Oracle Price Map Learning
Yingying Fan, Yuxuan Han, Jinchi Lv +2
We study contextual dynamic pricing in a semiparametric scalar-index valuation model where the latent value is , with an unknown utility map $μ_\ast…
LIDS: LLM Summary Inference Under the Layered Lens
Dylan Park, Yingying Fan, Jinchi Lv
Large language models (LLMs) have gained significant attention by many researchers and practitioners in natural language processing (NLP) since the introduction of ChatGPT in 2022.…
MOSAIC: Minimax-Optimal Sparsity-Adaptive Inference for Change Points in Dynamic Networks
Yingying Fan, Jingyuan Liu, Jinchi Lv +1
We propose a new inference framework, named MOSAIC, for change-point detection in dynamic networks with the simultaneous low-rank and sparse-change structure. We establish the mini…