In today's big data age, while the predict-then-optimize framework has become a standard method for tackling uncertain optimization challenges using machine learning tools, many prediction models overlook data intricacies such as outliers and heteroskedasticity. These oversights can degrade decision-...
This article introduces the Smart "Predict, then Optimize" (SPO) framework for developing prediction models within the predict-then-optimize paradigm. The framework leverages specialized loss functions that incorporate the structure of the optimization problem. It is applicable to problems with linear obj...
In contrast, we propose a new and very general framework, called Smart "Predict, then Optimize" (SPO), which directly leverages the optimization problem structure-that is, its objective and constraints-for designing better prediction models. A key component of our framework is the SPO loss ...
A key difference between the JNO framework and the baselines is that we utilize the structure of the correlation matrices to guide the predictive model. In essence, we optimize for the tradeoff between the neuroimaging and behavioral data representations jointly, instead of posing it as a two stag...
This input vector is then fed into a feed-forward neural network (FFNN) which maps f to predicted property Yp. To optimize the parameters in FFNN, we use the Mean Square Error (MSE) between predicted (Yp) and experimental (Ya) properties as shown in Fig. 5. The formula to calculate ...
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We optimize \(L\) via gradient descent using an Adam122 optimizer with learning rate 10−3. Model interpretation As candidate genes are predicted by an ensemble, we provide model interpretations based on the average importance of an edge or input feature across the whole ensemble. A related ...
The results obtained using a cross validation approach are used to optimize the hyper-parameters of the doc2vec model. The optimal configuration of these hyper parameters results in a doc2vec model where each country report will be transformed to a numerical vector of size 100. Once each ...
Next, the grid search optimization technique was utilized to optimize the hyperparameters. The objective of hyperparameter optimization is to refine and improve the model that delivers the highest and most accurate performance on a validation set. Data splitting and model development The dataset was ...
Paper tables with annotated results for DFF: Decision-Focused Fine-tuning for Smarter Predict-then-Optimize with Limited Data