
Nuria Gómez-VargasUniversity of Seville | US
Nuria Gómez-Vargas
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6
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Introduction
Currently working in the intersection of Machine Learning and Operations Research for Decision Making under Uncertainty
Skills and Expertise
Publications
Publications (6)
In this paper we address Contextual Multiobjective Inverse problems, a class of context-dependent optimization problems where limited information is available about their multiple objectives. These objectives are inferred from a training set comprising context-decision pairs. For each context, it is assumed that the corresponding decision is the on...
Robust Optimization (RO) provides a framework for making resilient decisions by constructing uncertainty sets for the parameters of an optimization problem. When the available parameter information is a simple random sample, uncertainty sets are constructed to ensure a high probability of coverage. However, many decision problems involve uncertain...
In real-world decision problems, the parameters that model either the objective function to be optimized (e.g., maximize profit) or the constraints (e.g., capacity) are usually subject to uncertainty. In Robust Optimization, a collection of problems with a common structure is considered, the parameters of the model are assumed to belong to a given...
In this paper, we introduce a novel predict-and-optimize method for profit-driven churn prevention. We frame the task of targeting customers for a retention campaign as a regret minimization problem. The main objective is to leverage individual customer lifetime values (CLVs) to ensure that only the most valuable customers are targeted. In contrast...
Individual re-identification is critical to track population changes in order to assess status, being particularly relevant in species with conservation concerns and difficult access like marine organisms. For this, we propose photo-identification via deep learning as a non-invasive technique to discriminate between individuals of the undulate skat...
Predict-and-optimize models address Operational Research problems with uncertain parameters by using state of the art Machine Learning models to predict such parameters, the prediction being embedded in the decision-making problem. In this paper we propose a new neural-networks-based Predict-and-optimize model, which trades off accuracy with explai...