montecarlomeasurements.jl
Propagation of distributions by Monte-Carlo sampling: Real number types with uncertainty represented by samples.
https://github.com/cog-imperial/romodel
Modeling robust optimization problems in Pyomo
https://github.com/anishacharya/bgmd-aistats-2022
Geometric median (GM) is a classical method in statistics for achieving a robust estimation of the uncorrupted data; under gross corruption, it achieves the optimal breakdown point of 0.5. However, its computational complexity makes it infeasible for robustifying stochastic gradient descent (SGD) for high-dimensional optimization problems. In this paper, we show that by applying Gm to only a judiciously chosen block of coordinates at a time and using a memory mechanism, one can retain the breakdown point of 0.5 for smooth non-convex problems, with non-asymptotic convergence rates comparable to the SGD with GM.
