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Found 9 papers, 6 papers with code
From Large Language Models and Optimization to Decision Optimization CoPilot: A Research Manifesto
Significantly simplifying the creation of optimization models for real-world business problems has long been a major goal in applying mathematical optimization more widely to important business and societal decisions.
Neur2BiLO: Neural Bilevel Optimization
Bilevel optimization deals with nested problems in which a leader takes the first decision to minimize their objective function while accounting for a follower's best-response reaction.
Neur2RO: Neural Two-Stage Robust Optimization
This work addresses two-stage robust optimization (2RO) problems (also called adjustable robust optimization), wherein first-stage and second-stage decisions are made before and after uncertainty is realized, respectively.
Finding Regions of Counterfactual Explanations via Robust Optimization
To this end, our method provides a whole region of CEs allowing the user to choose a suitable recourse to obtain a desired outcome.
Data-driven Prediction of Relevant Scenarios for Robust Combinatorial Optimization
We study iterative methods for (two-stage) robust combinatorial optimization problems with discrete uncertainty.
Ensemble Methods for Robust Support Vector Machines using Integer Programming
We study robust support vector machines (SVM) and extend the classical approach by an ensemble method which iteratively solves a non-robust SVM on different perturbations of the dataset, where the perturbations are derived by an adversarial problem.
Efficient and Robust Mixed-Integer Optimization Methods for Training Binarized Deep Neural Networks
Compared to classical deep neural networks its binarized versions can be useful for applications on resource-limited devices due to their reduction in memory consumption and computational demands.
Data-Driven Robust Optimization using Unsupervised Deep Learning
Robust optimization has been established as a leading methodology to approach decision problems under uncertainty.
An Integer Programming Approach to Deep Neural Networks with Binary Activation Functions
We study deep neural networks with binary activation functions (BDNN), i. e. the activation function only has two states.
