Posted on April 19, 2021 by Raghu Rajan, André Biedenkapp, Frederic Runge, Jörg Franke
Reinforcement learning (RL) has shown impressive results in a variety of applications. Well known examples include game and video game playing, robotics and, recently, “Autonomous navigation of stratospheric balloons”. A lot of the successes came about by combining the expressiveness of deep learning with the power of RL. Already on their own though, both frameworks […]
Posted on August 5, 2020 by André Biedenkapp
In a Nutshell In CMA-ES, the step size controls how fast or slow a population traverses through a search space. Large steps allow you to quickly skip over uninteresting areas (exploration), whereas small steps allow a more focused traversal of interesting areas (exploitation). Handcrafted heuristics usually trade off small and large steps given some measure […]
Posted on February 10, 2020 by André Biedenkapp
By Motivation When designing algorithms we want them to be as flexible as possible such that they can solve as many problems as possible. To solve a specific family of problems well, finding well-performing hyperparameter configurations requires us to either use extensive domain knowledge or resources. The second point is especially true if we […]
Posted on July 26, 2018 by André Biedenkapp, Frank Hutter
By 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 set its learning […]
Posted on July 13, 2018 by André Biedenkapp, Katharina Eggensperger, Matthias Feurer, Frank Hutter
By 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.
