5 papers · 1 filter
Latent Space Inference via Paired Autoencoders
Emma Hart, Bas Peters, Julianne Chung +1
This work describes a novel data-driven latent space inference framework built on paired autoencoders to handle observational inconsistencies when solving inverse problems. Our app…
Class Incremental Learning for Algorithm Selection
Mate Botond Nemeth, Emma Hart, Kevin Sim +1
Algorithm selection is commonly used to predict the best solver from a portfolio per per-instance. In many real scenarios, instances arrive in a stream: new instances become availa…
A Paired Autoencoder Framework for Inverse Problems via Bayes Risk Minimization
Emma Hart, Julianne Chung, Matthias Chung
In this work, we describe a new data-driven approach for inverse problems that exploits technologies from machine learning, in particular autoencoder network structures. We conside…
Algorithm Selection with Probing Trajectories: Benchmarking the Choice of Classifier Model
Quentin Renau, Emma Hart
Recent approaches to training algorithm selectors in the black-box optimisation domain have advocated for the use of training data that is algorithm-centric in order to encapsulate…
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…