Hyperparameter Trajectory Inference with Conditional Lagrangian Optimal Transport

TL;DR

Neural networks (NNs) often have critical behavioural trade-offs that are set at design time with hyperparameters—such as reward weights in reinforcement learning or quantile targets in regression. Post-deployment, however, user preferences can evolve, making initial settings undesirable, necessitating potentially expensive retraining.

Opening excerpt from the authors’ abstract. source