Decision-Focused Learning (DFL) is a paradigm for tailoring a predictive model to a downstream optimisation task that uses its predictions, so that it can perform better on that specific task.
We introduce a novel decomposed GP regression to incorporate the subgroup decomposed feedback.
For example, case counts may be sparse when only a small fraction of infections are caught by a testing program.
Solving optimization problems with unknown parameters often requires learning a predictive model to predict the values of the unknown parameters and then solving the problem using these values.
We end with experiments on two datasets that utilise both the topological and fuzzy nature of our algorithm: pre-trained model selection in machine learning and lattices structures from materials science.
It has been successfully applied to several limited combinatorial problem classes, such as those that can be expressed as linear programs (LP), and submodular optimization.
A serious challenge when finding influential actors in real-world social networks is the lack of knowledge about the structure of the underlying network.
However, graphs or related attributes are often only partially observed, introducing learning problems such as link prediction which must be solved prior to optimization.
We demonstrate that by integrating this solver into end-to-end learning systems, we can learn the logical structure of challenging problems in a minimally supervised fashion.
Ranked #1 on Game of Suduko on Sudoko 9x9
Influence maximization is a widely used model for information dissemination in social networks.
Computer Science and Game Theory Social and Information Networks
Stackelberg security games are a critical tool for maximizing the utility of limited defense resources to protect important targets from an intelligent adversary.
Digital Adherence Technologies (DATs) are an increasingly popular method for verifying patient adherence to many medications.
These components are typically approached separately: a machine learning model is first trained via a measure of predictive accuracy, and then its predictions are used as input into an optimization algorithm which produces a decision.
Social and behavioral interventions are a critical tool for governments and communities to tackle deep-rooted societal challenges such as homelessness, disease, and poverty.