Machine Learning and Inverse Problems: Depth and Model Adaptation
Rebecca Willett
University of Chicago
Instructor: Rebecca Willett
Professor, Department of Electrical and Computer Engineering, UW-Madison.
A data-dependent weighted LASSO
Sparse linear inverse problems appear in a variety of settings, but often the noise contaminating observations cannot accurately be described as bounded or arising from a Gaussian distribution. Poisson observations in particular are a characteristic feature of several real-world applications. Previous work on sparse Poisson inverse problems encountered several limiting …
The Dark Side of Image Reconstruction: Emerging Methods in Photon-Limited Imaging
Many scientific and engineering applications rely upon the accurate reconstruction of spatially, spectrally, and temporally distributed phenomena from photon-limited data. When the number of observed events is very small, accurately extracting knowledge from this data requires the development of both new computational methods and novel theoretical analysis frameworks. This task …
Split Cuts for Two-Stage Stochastic Integer Programs and Tracking Influence in Dynamic Social Networks
Merve Bodur Stochastic programming is a way of dealing with uncertainty in the optimization problems. We consider two-stage stochastic programs with integer first stage and continuous second stage. It means that the decision maker must take some integer decisions before the uncertainty is revealed, then can observe the realizations and …