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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 …
End-to-end constrained optimization learning: A survey
This paper surveys the recent attempts at leveraging machine learning to solve constrained
optimization problems. It focuses on surveying the work on integrating combinatorial solvers …
optimization problems. It focuses on surveying the work on integrating combinatorial solvers …
Combinatorial optimization and reasoning with graph neural networks
Combinatorial optimization is a well-established area in operations research and computer
science. Until recently, its methods have focused on solving problem instances in isolation …
science. Until recently, its methods have focused on solving problem instances in isolation …
Decision-focused learning: Foundations, state of the art, benchmark and future opportunities
Decision-focused learning (DFL) is an emerging paradigm that integrates machine learning
(ML) and constrained optimization to enhance decision quality by training ML models in an …
(ML) and constrained optimization to enhance decision quality by training ML models in an …
Implicit MLE: backpropagating through discrete exponential family distributions
Combining discrete probability distributions and combinatorial optimization problems with
neural network components has numerous applications but poses several challenges. We …
neural network components has numerous applications but poses several challenges. We …
Decision-focused learning: Through the lens of learning to rank
In the last years decision-focused learning framework, also known as predict-and-optimize,
have received increasing attention. In this setting, the predictions of a machine learning …
have received increasing attention. In this setting, the predictions of a machine learning …
Pyepo: A pytorch-based end-to-end predict-then-optimize library for linear and integer programming
In deterministic optimization, it is typically assumed that all problem parameters are fixed
and known. In practice, however, some parameters may be a priori unknown but can be …
and known. In practice, however, some parameters may be a priori unknown but can be …
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 …
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 …
Learning with combinatorial optimization layers: a probabilistic approach
Combinatorial optimization (CO) layers in machine learning (ML) pipelines are a powerful
tool to tackle data-driven decision tasks, but they come with two main challenges. First, the …
tool to tackle data-driven decision tasks, but they come with two main challenges. First, the …