6 citations · 6 across the 8 of their papers we have counts for
8 papers
Automated Design of Inventory Policy with Large Language Models: An Exploratory Study
Fenghua Yang, Preet Baxi, Yi Zhang +4
Firms making inventory decisions have access to operational data, optimization tools, and large language models (LLMs). Typically, data characterize the operating environment, opti…
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…
Sequential Sampling for Binary Classification: Two LLMs are (Almost) All You Need
Guokai Li, Alys Liang, Mo Liu +4
We study how to orchestrate multiple large language models (LLMs) for reliable binary classification when models differ in accuracy, monetary cost, and response time. A decision-ma…
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…
A Re-solving Heuristic for Dynamic Assortment Optimization with Knapsack Constraints
Xi Chen, Mo Liu, Yining Wang +1
In this paper, we consider a multi-stage dynamic assortment optimization problem with multi-nomial choice modeling (MNL) under resource knapsack constraints. Given the current reso…
Active Learning For Contextual Linear Optimization: A Margin-Based Approach
Mo Liu, Paul Grigas, Heyuan Liu +1
We develop the first active learning method for contextual linear optimization. Specifically, we introduce a label acquisition algorithm that sequentially decides whether to reques…