01.05.2022 − 30.04.2025
The relatively newer space of Multiobjective machine learning has focused on Bayesian optimization, with tuning limited to the space of model hyperparameters. Examples of objectives include accuracy, inference time, and complexity. We aim to develop tractable optimization formulations to address the extensive tunable space of models, hyperparameters, model parameters, and feature engineering embodiments. We will also address questions related to multi-label classification, regression, and algorithmic bias (example: fairness). From an Implementation perspective, our focus will be on developing tailored versions of solvers like SGD (by exploitation of problem structure). Our main applications of interest include problems in energy markets and imaging.