CS4ML: A general framework for active learning with arbitrary data based on Christoffel functions

1 June 2023

Papers citing "CS4ML: A general framework for active learning with arbitrary data based on Christoffel functions"

7 / 7 papers shown

Title
Denoising guarantees for optimized sampling schemes in compressed sensing Y. Plan Matthew Scott Xia Sheng Özgür Yilmaz 25 0 0 01 Apr 2025
Provably Accurate Shapley Value Estimation via Leverage Score Sampling Christopher Musco R. Teal Witter FAtt FedML TDI 44 2 0 02 Oct 2024
Agnostic Active Learning of Single Index Models with Linear Sample Complexity Aarshvi Gajjar Wai Ming Tai Xingyu Xu Chinmay Hegde Yi Li Chris Musco 29 2 0 15 May 2024
A unified framework for learning with nonlinear model classes from arbitrary linear samples Ben Adcock Juan M. Cardenas N. Dexter 18 3 0 25 Nov 2023
Model-adapted Fourier sampling for generative compressed sensing Aaron Berk Simone Brugiapaglia Y. Plan Matthew Scott Xia Sheng Özgür Yilmaz 16 2 0 08 Oct 2023
Non-Iterative Recovery from Nonlinear Observations using Generative Models Jiulong Liu Zhaoqiang Liu 23 11 0 31 May 2022
Generic bounds on the approximation error for physics-informed (and) operator learning Tim De Ryck Siddhartha Mishra PINN 56 58 0 23 May 2022