Hyperparameter Tuning with Constrained Bayesian Optimization
Posterior Objective FunctionDescription
The objective of the project is to determine the value of a model hyperparameter that maximizes the validation accuracy subject to a constraint on the average prediction speed. To this end, Bayesian Optimization with Expected Improvement (EI) and Upper Confidence Bound (UCB) acquisition functions are employed to implement the algorithm.
Chenhao Li
Reinforcement Learning for Robotics
My research interests focus on the general field of robot learning, including reinforcement learning, developmental robotics and legged intelligence.