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Found 3 papers, 1 papers with code
Probabilistic Forecasting with Coherent Aggregation
Our model uses a convolutional neural network to produce parameters for the factors, their loadings and base-level distributions; it produces samples which can be differentiated with respect to the model's parameters; and it can therefore optimize for any sample-based loss function, including the Continuous Ranked Probability Score and quantile losses.
Learning differentiable solvers for systems with hard constraints
Our method leverages differentiable optimization and the implicit function theorem to effectively enforce physical constraints.
Stochastic Frank-Wolfe for Constrained Finite-Sum Minimization
We propose a novel Stochastic Frank-Wolfe (a. k. a.
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