{"id":173,"date":"2020-01-24T15:17:04","date_gmt":"2020-01-24T15:17:04","guid":{"rendered":"http:\/\/www.lancaster.ac.uk\/stor-i-student-sites\/tamas-papp\/?p=173"},"modified":"2020-04-20T17:57:47","modified_gmt":"2020-04-20T17:57:47","slug":"multi-fidelity-bayesian-optimisation","status":"publish","type":"post","link":"https:\/\/www.lancaster.ac.uk\/stor-i-student-sites\/tamas-papp\/2020\/01\/24\/multi-fidelity-bayesian-optimisation\/","title":{"rendered":"Multi-fidelity Bayesian optimisation"},"content":{"rendered":"\n
Say you\u2019re a robotics researcher and you want to find the best path for a robot to take in order to perform some tasks. Unless your course is very simple, you\u2019ll probably have to fiddle with some parameters in order to achieve this with confidence, and so you\u2019ve decided to use Bayesian optimisation<\/a> to cut down on time. You still need to run your robot on the full course every time you evaluate, so if you need good results very quickly this still won\u2019t cut it.<\/p>\n\n\n\n However, if you can perform a fast simulation of your robot’s behaviour that reasonably is close to the truth, you might consider somehow incorporating it into your procedure to speed things up. Enter the domain of multi-fidelity optimisation, where rough-and-ready approximations of the objective function are used to cut down on evaluation time. As long as you can quantify how much information you get from these low-fidelity sources (a non-trivial task!), you can add them to your routine.<\/p>\n\n\n\n