[HTML][HTML] Forecasting: theory and practice
Forecasting has always been at the forefront of decision making and planning. The
uncertainty that surrounds the future is both exciting and challenging, with individuals and …
uncertainty that surrounds the future is both exciting and challenging, with individuals and …
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 …
Differentiation of blackbox combinatorial solvers
Achieving fusion of deep learning with combinatorial algorithms promises transformative
changes to artificial intelligence. One possible approach is to introduce combinatorial …
changes to artificial intelligence. One possible approach is to introduce combinatorial …
Differentiation of blackbox combinatorial solvers
Achieving fusion of deep learning with combinatorial algorithms promises transformative
changes to artificial intelligence. One possible approach is to introduce combinatorial …
changes to artificial intelligence. One possible approach is to introduce combinatorial …
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 …
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 …
[HTML][HTML] Integrating prediction with optimization: Models and applications in transportation management
Prediction and optimization are the foundation of many real-world analytics problems in
various disciplines. As both can be challenging, they are usually treated sequentially in …
various disciplines. As both can be challenging, they are usually treated sequentially in …
[HTML][HTML] Optimization with constraint learning: A framework and survey
Many real-life optimization problems frequently contain one or more constraints or objectives
for which there are no explicit formulae. If however data on feasible and/or infeasible states …
for which there are no explicit formulae. If however data on feasible and/or infeasible states …
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 …