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A survey of contextual optimization methods for decision-making under uncertainty
U Sadana, A Chenreddy, E Delage, A Forel… - European Journal of …, 2024 - Elsevier
Recently there has been a surge of interest in operations research (OR) and the machine
learning (ML) community in combining prediction algorithms and optimization techniques to …
learning (ML) community in combining prediction algorithms and optimization techniques to …
Inverse optimization: Theory and applications
Inverse optimization describes a process that is the “reverse” of traditional mathematical
optimization. Unlike traditional optimization, which seeks to compute optimal decisions given …
optimization. Unlike traditional optimization, which seeks to compute optimal decisions given …
A survey of contextual optimization methods for decision making under uncertainty
Recently there has been a surge of interest in operations research (OR) and the machine
learning (ML) community in combining prediction algorithms and optimization techniques to …
learning (ML) community in combining prediction algorithms and optimization techniques to …
Generalization bounds in the predict-then-optimize framework
The predict-then-optimize framework is fundamental in many practical settings: predict the
unknown parameters of an optimization problem, and then solve the problem using the …
unknown parameters of an optimization problem, and then solve the problem using the …
Predict-then-calibrate: A new perspective of robust contextual lp
Contextual optimization, also known as predict-then-optimize or prescriptive analytics,
considers an optimization problem with the presence of covariates (context or side …
considers an optimization problem with the presence of covariates (context or side …
Integrated conditional estimation-optimization
Many real-world optimization problems involve uncertain parameters with probability
distributions that can be estimated using contextual feature information. In contrast to the …
distributions that can be estimated using contextual feature information. In contrast to the …
Fast rates for contextual linear optimization
Incorporating side observations in decision making can reduce uncertainty and boost
performance, but it also requires that we tackle a potentially complex predictive relationship …
performance, but it also requires that we tackle a potentially complex predictive relationship …
Risk bounds and calibration for a smart predict-then-optimize method
The predict-then-optimize framework is fundamental in practical stochastic decision-making
problems: first predict unknown parameters of an optimization model, then solve the problem …
problems: first predict unknown parameters of an optimization model, then solve the problem …
Maximum optimality margin: A unified approach for contextual linear programming and inverse linear programming
In this paper, we study the predict-then-optimize problem where the output of a machine
learning prediction task is used as the input of some downstream optimization problem, say …
learning prediction task is used as the input of some downstream optimization problem, say …
Active learning in the predict-then-optimize framework: A margin-based approach
We develop the first active learning method in the predict-then-optimize framework.
Specifically, we develop a learning method that sequentially decides whether to request the" …
Specifically, we develop a learning method that sequentially decides whether to request the" …