Interpolation Learning

Interpolation learning focuses on developing methods that effectively generate intermediate values or predictions between known data points, addressing challenges in areas like time series forecasting, signal processing, and quantile estimation. Current research emphasizes the use of neural networks, particularly recurrent networks and diffusion models, along with novel interpolation algorithms tailored to specific data types and application domains, such as learning-based interpolation agents for denoising and point convolutions for surface reconstruction. These advancements improve the accuracy and efficiency of predictions and estimations in various fields, offering significant improvements over traditional methods, particularly in high-dimensional or noisy data scenarios.