Dec 10, 2023
Speaker · 0 followers
Speaker · 0 followers
Speaker · 0 followers
We introduce a general framework for active learning in regression problems. Our framework extends the standard setup by allowing for general types of data, rather than merely pointwise samples of the target function. This generalization covers many cases of practical interest, such as data acquired in transform domains (e.g., Fourier data), vector-valued data (e.g., gradient-augmented data), data acquired along continuous curves, and, multimodal data (i.e., combinations of different types of measurements). Our framework considers random sampling according to a finite number of sampling measures and arbitrary nonlinear approximation spaces (model classes). We introduce the concept of generalized Christoffel functions and show how these can be used to optimize the sampling measures. We prove that this leads to near-optimal sample complexity in various important cases. This paper focuses on applications in scientific computing, where active learning is often desirable, since it is usually expensive to generate data. We demonstrate the efficacy of our framework for gradient-augmented learning with polynomials, Magnetic Resonance Imaging (MRI) using generative models and adaptive sampling for solving PDEs using Physics-Informed Neural Networks (PINNs).We introduce a general framework for active learning in regression problems. Our framework extends the standard setup by allowing for general types of data, rather than merely pointwise samples of the target function. This generalization covers many cases of practical interest, such as data acquired in transform domains (e.g., Fourier data), vector-valued data (e.g., gradient-augmented data), data acquired along continuous curves, and, multimodal data (i.e., combinations of different types of me…
Account · 622 followers
Professional recording and live streaming, delivered globally.
Presentations on similar topic, category or speaker
Zhi Li, …
Total of 0 viewers voted for saving the presentation to eternal vault which is 0.0%
Mengkang Lu, …
Total of 0 viewers voted for saving the presentation to eternal vault which is 0.0%
Total of 0 viewers voted for saving the presentation to eternal vault which is 0.0%
Mark Schoene, …
Total of 0 viewers voted for saving the presentation to eternal vault which is 0.0%
Lujie Xia, …
Total of 0 viewers voted for saving the presentation to eternal vault which is 0.0%
Nils Sturma, …
Total of 0 viewers voted for saving the presentation to eternal vault which is 0.0%