Learning analytical models from noisy data remains challenging and depends essentially on the noise level. The authors analyze the transition of the model-learning problem from a low-noise phase to a phase where noise is too high for the underlying model to be learned by any method, and estimate upper bounds for the transition noise.
- Oscar Fajardo-Fontiveros
- Ignasi Reichardt
- Roger GuimerĂ