UC Santa Barbara

Deep neural networks usually have many hyperparameters that need to be tuned. Modern material design problems usually require material scientists to sequentially select processing parameters and conduct experiments to observe material performances. To save privacy cost, the learning system needs to carefully choose queries to answer under the differential privacy framework. To train a robot under video guidance, engineers need to carefully choose video samples for training. However, in all cases, people cannot observe performances of unselected actions and the experimental cost can be huge. These two challenges hinder efficient neural network training, new material design, privacy protection, and robot training and call for actions. In this thesis, I present my research on optimization, bandits, and active learning under the adaptive sequential decision making framework. My algorithms are able to solve black box function optimization without the curse of dimensionality, achieve no regret under the function class misspecification, reduce privacy cost under the differential privacy framework, and significantly reduce video sample complexity for robot training. All of them come with theoretical or empirical analysis.