3 papers
cs.LG2026
Decision-Driven Regularization: A Blended Model for Learning and Optimization
Gar Goei Loke, Qinshen Tang, Yangge Xiao +1
In contextual optimization, the decision-maker seeks optimal decisions to minimize a cost function, that varies based on observed features. This context is common in many business…
cs.LG2026
Autocorrelated Optimize-via-Estimate: Predict-then-Optimize versus Finite-sample Optimal
Zichun Wang, Gar Goei Loke, Ruiting Zuo
Models that directly optimize for out-of-sample performance in the finite-sample regime have emerged as a promising alternative to traditional estimate-then-optimize approaches in…
math.OC2023
Optimize-via-Predict: Realizing out-of-sample optimality in data-driven optimization
Gar Goei Loke, Taozeng Zhu, Ruiting Zuo
We examine a stochastic formulation for data-driven optimization wherein the decision-maker is not privy to the true distribution, but has knowledge that it lies in some hypothesis…