The ixAutoML project has introduced several novel methodologies to address the challenges of trust and accessibility in AutoML.
A key methodological innovation is the development of symbolic explanation techniques for hyperparameter optimization based on performance data collected via Bayesian Optimization, making complex AutoML decisions comprehensible for users. In addition, we developed a new variant of Bayesian Optimization (BO) that re-interprets the Bayesian Execution Algorithm (BAX) to obtain high-quality partial dependence plots (PDPs). By interleaving BAX with traditional BO, we can show that the optimization performance does not decrease while obtaining much better PDPs.
We are the first to propose a systematic approach for studying the hyperparameter landscapes of reinforcement learning algorithms dynamically during policy training. Since at each decision point, we could spawn a large number of new policy trainings, we are facing a combinatorial problem. We propose to store checkpoints and continue policy learning from there until convergence. By choosing the best-performing policies out of all hyperparameter configurations, we can go back to a checkpoint and spawn new hyperparameter configuration runs from there. This technique is overall very efficient and allows a deeper understanding of the landscapes.
To improve the efficiency, robustness, and soundness of AutoRL systems, we propose a recipe for efficient RL research, incl. training test splits on different levels, designing configuration spaces, deciding on the correct optimization approach, settling on an important cost metric, running HPO for several seeds and evaluate on new seeds. Although, maybe surprisingly trivial, we were able to show that results in comparing RL and AutoRL algorithms improved in quality substantially.
To enable interaction between users and AutoML packages, we propose to bias hyperparameter optimization towards users' expectations (e.g. preferred learning rates). In contrast to previous approaches, we proposed a simple-to-implement generic approach based on re-weighting the acquisition function w.r.t. the user prior that can outperform complex previous approaches and keeps the theoretical guarantees for BO.
Furthermore, we have established an interactive framework for hyperparameter optimization (HPO), leveraging preference learning to decide between examples of Pareto fronts. The learned preferences can then be used to guide HPO towards preferred shapes of Pareto Fronts. This allows human users to actively guide the HPO process in multi-objective settings, creating a seamless integration of human intuition and algorithmic efficiency.
