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Found 3 papers, 1 papers with code
Functional Output Regression with Infimal Convolution: Exploring the Huber and $ε$-insensitive Losses
The efficiency of the approach is demonstrated and contrasted with the classical squared loss setting on both synthetic and real-world benchmarks.
Wind power predictions from nowcasts to 4-hour forecasts: a learning approach with variable selection
We study short-term prediction of wind speed and wind power (every 10 minutes up to 4 hours ahead).
Nonlinear Functional Output Regression: a Dictionary Approach
Then, in the more general setting of integral losses based on differentiable ground losses, KPL is implemented using first-order optimization for both fully and partially observed output functions.
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