Perspectives on Bayesian Optimization for HCI

In this position paper we discuss optimization in the HCI domain based on our experiences with Bayesian methods for modeling and optimization of audio systems, including challenges related to evaluating, designing, and optimizing such interfaces. We outline and demonstrate how a combined Bayesian modeling and optimization approach provides a flexible framework for integrating various user and content attributes, while also supporting model-based optimization of HCI systems. Finally, we discuss current and future research direction and applications, such as inferring user needs and optimizing interfaces for computer assisted teaching

Active learning for feasible region discovery

Often in the design process of an engineer, the design specifications of the system are not completely known initially. However, usually there are some physical constraints which are already known, corresponding to a region of interest in the design space that is called feasible. These constraints often have no analytical form but need to be characterised based on expensive simulations or measurements. Therefore, it is important that the feasible region can be modeled sufficiently accurate using only a limited amount of samples. This can be solved by using active learning techniques that minimize the amount of samples w.r.t. what we try to model. Most active learning strategies focus on classification models or regression models with classification accuracy and regression accuracy in mind respectively. In this work, regression models of the constraints are used, but only the (in) feasibility is of interest. To tackle this problem, an information-theoretic sampling strategy is constructed to discover these regions. The proposed method is then tested on two synthetic examples and one engineering example and proves to outperform the current state-of-the-art

Perspectives on Bayesian Optimization for HCI

In this position paper we discuss optimization in the HCI domain based on our experiences with Bayesian methods for modeling and optimization of audio systems, including challenges related to evaluating, designing, and optimizing such interfaces. We outline and demonstrate how a combined Bayesian modeling and optimization approach provides a flexible framework for integrating various user and content attributes, while also supporting model-based optimization of HCI systems. Finally, we discuss current and future research direction and applications, such as inferring user needs and optimizing interfaces for computer assisted teaching

Predictive Entropy Search for Efficient Global Optimization of Black-box Functions

We propose a novel information-theoretic approach for Bayesian optimization called Predictive Entropy Search (PES). At each iteration, PES selects the next evaluation point that maximizes the expected information gained with respect to the global maximum. PES codifies this intractable acquisition function in terms of the expected reduction in the differential entropy of the predictive distribution. This reformulation allows PES to obtain approximations that are both more accurate and efficient than other alternatives such as Entropy Search (ES). Furthermore, PES can easily perform a fully Bayesian treatment of the model hyperparameters while ES cannot. We evaluate PES in both synthetic and real-world applications, including optimization problems in machine learning, finance, biotechnology, and robotics. We show that the increased accuracy of PES leads to significant gains in optimization performance

Batch Bayesian active learning for feasible region identification by local penalization

Identifying all designs satisfying a set of constraints is an important part of the engineering design process. With physics-based simulation codes, evaluating the constraints becomes considerable expensive. Active learning can provide an elegant approach to efficiently characterize the feasible region, i.e., the set of feasible designs. Although active learning strategies have been proposed for this task, most of them are dealing with adding just one sample per iteration as opposed to selecting multiple samples per iteration, also known as batch active learning. While this is efficient with respect to the amount of information gained per iteration, it neglects available computation resources. We propose a batch Bayesian active learning technique for feasible region identification by assuming that the constraint function is Lipschitz continuous. In addition, we extend current state-of-the-art batch methods to also handle feasible region identification. Experiments show better performance of the proposed method than the extended batch methods