8 papers
Decision-Focused Bias Correction for Fluid Approximation
Can Er, Mo Liu
We revisit the multi-period newsvendor network problem, in which demands from multiple customers are correlated and jointly time-varying. Due to the curse of dimensionality associa…
Decision-Focused Learning: When and Why Traditional Prediction Models Fail
Mo Liu
Plugging predictions of unknown parameters into downstream optimization problems, often referred to as the ``predict-then-optimize'' paradigm, has long been a standard approach in…
A Solver-Free Training Method for Predict-then-Optimize
Beichen Wan, Mo Liu
We propose a scalable method for training prediction (machine learning) models in the predict-then-optimize paradigm, where model outputs serve as coefficients for a subsequent lin…
Asymptotically Optimal Sequential Testing with Heterogeneous LLMs
Guokai Li, Alys Liang, Mo Liu +4
We study a Bayesian binary sequential hypothesis testing problem with multiple large language models (LLMs). Each LLM has per-query cost , random waiting time with mean…
Decision-Focused Sequential Experimental Design: A Directional Uncertainty-Guided Approach
Beichen Wan, Mo Liu, Paul Grigas +1
We consider the sequential experimental design problem in the predict-then-optimize paradigm. In this paradigm, the outputs of the prediction model are used as coefficient vectors…
Decision-Focused Optimal Transport
Suhan Liu, Mo Liu
We propose a fundamental metric for measuring the distance between two distributions. This metric, referred to as the decision-focused (DF) divergence, is tailored to stochastic li…