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 …
Surco: Learning linear surrogates for combinatorial nonlinear optimization problems
Optimization problems with nonlinear cost functions and combinatorial constraints appear in
many real-world applications but remain challenging to solve efficiently compared to their …
many real-world applications but remain challenging to solve efficiently compared to their …
Decision-focused learning without decision-making: Learning locally optimized decision losses
Abstract Decision-Focused Learning (DFL) is a paradigm for tailoring a predictive model to a
downstream optimization task that uses its predictions in order to perform better\textit {on that …
downstream optimization task that uses its predictions in order to perform better\textit {on that …
Generalization bounds in the predict-then-optimize framework
O El Balghiti, AN Elmachtoub… - Advances in neural …, 2019 - proceedings.neurips.cc
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 …
End-to-end stochastic optimization with energy-based model
Decision-focused learning (DFL) was recently proposed for stochastic optimization problems
that involve unknown parameters. By integrating predictive modeling with an implicitly …
that involve unknown parameters. By integrating predictive modeling with an implicitly …
End-to-end learning to warm-start for real-time quadratic optimization
First-order methods are widely used to solve convex quadratic programs (QPs) in real-time
appli-cations because of their low per-iteration cost. However, they can suffer from slow …
appli-cations because of their low per-iteration cost. However, they can suffer from slow …
Learning mdps from features: Predict-then-optimize for sequential decision making by reinforcement learning
In the predict-then-optimize framework, the objective is to train a predictive model, map**
from environment features to parameters of an optimization problem, which maximizes …
from environment features to parameters of an optimization problem, which maximizes …
Leaving the nest: Going beyond local loss functions for predict-then-optimize
Predict-then-Optimize is a framework for using machine learning to perform decision-making
under uncertainty. The central research question it asks is," How can we use the structure of …
under uncertainty. The central research question it asks is," How can we use the structure of …
Decision-aware learning for optimizing health supply chains
Decision-focused Graph Neural Networks for Graph Learning and Optimization
Decision-focused learning (DFL) combines both machine learning and combinatorial
optimization so as to enhance the quality of decision-making. In general, DFL adds an …
optimization so as to enhance the quality of decision-making. In general, DFL adds an …