By Frederic Runge and Danny Stoll In reinforcement learning (RL), one of the major machine learning (ML) paradigms, an agent interacts with an environment. How well an RL agent can solve a problem, can be sensitive to choices such as the policy network architecture, the training hyperparameters, or the specific dynamics of the environment. A […]
By André Biedenkapp, Frank Hutter Machine learning has achieved many successes in a wide range of application areas, but more often than not, these strongly rely on choosing the correct values for many hyperparameters (see e.g. Snoek et al., 2012). For example, we all know of the awesome results deep learning can achieve, but when we […]
By André Biedenkapp, Katharina Eggensperger, Matthias Feurer, Frank Hutter Our ML Freiburg lab is the world champion in automatic machine learning (AutoML) again! After winning the first international AutoML challenge (2015-2016), we also just won the second international AutoML challenge (2017-2018). Our system PoSH-Auto-sklearn outperformed all other 41 participating AutoML systems. What is AutoML and […]