Tackling climate change with machine learning
Climate change is one of the greatest challenges facing humanity, and we, as machine
learning (ML) experts, may wonder how we can help. Here we describe how ML can be a …
learning (ML) experts, may wonder how we can help. Here we describe how ML can be a …
End-to-end constrained optimization learning: A survey
J Kotary, F Fioretto, P Van Hentenryck… - arXiv preprint arXiv …, 2021 - arxiv.org
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 …
Differentiable convex optimization layers
Recent work has shown how to embed differentiable optimization problems (that is,
problems whose solutions can be backpropagated through) as layers within deep learning …
problems whose solutions can be backpropagated through) as layers within deep learning …
DC3: A learning method for optimization with hard constraints
Large optimization problems with hard constraints arise in many settings, yet classical
solvers are often prohibitively slow, motivating the use of deep networks as cheap" …
solvers are often prohibitively slow, motivating the use of deep networks as cheap" …
Satnet: Bridging deep learning and logical reasoning using a differentiable satisfiability solver
Integrating logical reasoning within deep learning architectures has been a major goal of
modern AI systems. In this paper, we propose a new direction toward this goal by …
modern AI systems. In this paper, we propose a new direction toward this goal by …
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 …
Machine learning for sustainable energy systems
In recent years, machine learning has proven to be a powerful tool for deriving insights from
data. In this review, we describe ways in which machine learning has been leveraged to …
data. In this review, we describe ways in which machine learning has been leveraged to …
Smart predict-and-optimize for hard combinatorial optimization problems
Combinatorial optimization assumes that all parameters of the optimization problem, eg the
weights in the objective function, are fixed. Often, these weights are mere estimates and …
weights in the objective function, are fixed. Often, these weights are mere estimates and …
Decision trees for decision-making under the predict-then-optimize framework
AN Elmachtoub, JCN Liang… - … conference on machine …, 2020 - proceedings.mlr.press
We consider the use of decision trees for decision-making problems under the predict-then-
optimize framework. That is, we would like to first use a decision tree to predict unknown …
optimize framework. That is, we would like to first use a decision tree to predict unknown …
Mipaal: Mixed integer program as a layer
Abstract Machine learning components commonly appear in larger decision-making
pipelines; however, the model training process typically focuses only on a loss that …
pipelines; however, the model training process typically focuses only on a loss that …