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cs.LG2024
Identifying Easy Instances to Improve Efficiency of ML Pipelines for Algorithm-Selection
Quentin Renau, Emma Hart
Algorithm-selection (AS) methods are essential in order to obtain the best performance from a portfolio of solvers over large sets of instances. However, many AS methods rely on an…
cs.LG2024
Paired Autoencoders for Likelihood-free Estimation in Inverse Problems
Matthias Chung, Emma Hart, Julianne Chung +2
We consider the solution of nonlinear inverse problems where the forward problem is a discretization of a partial differential equation. Such problems are notoriously difficult to…
cs.LG2024
On the Utility of Probing Trajectories for Algorithm-Selection
Quentin Renau, Emma Hart
Machine-learning approaches to algorithm-selection typically take data describing an instance as input. Input data can take the form of features derived from the instance descripti…