Physically Consistent Preferential Bayesian Optimization for Food Arrangement

This paper considers the problem of estimating a preferred food arrangement for users from interactive pairwise comparisons using Computer Graphics (CG)-based dish images. As a foodservice industry requirement, we need to utilize domain rules for the geometry of the arrangement (e.g., the food layout of some Japanese dishes is reminiscent of mountains). However, those rules are qualitative and ambiguous; the estimated result might be physically inconsistent (e.g., each food physically interferes, and the arrangement becomes infeasible). To cope with this problem, we propose Physically Consistent Preferential Bayesian Optimization (PCPBO) as a method that obtains physically feasible and preferred arrangements that satisfy domain rules. PCPBO employs a bi-level optimization that combines a physical simulation-based optimization and a Preference-based Bayesian Optimization (PbBO). Our experimental results demonstrated the effectiveness of PCPBO on simulated and actual human users.Comment: 8 pages, 10 figures, accepted by IEEE Robotics and Automation Letters (RA-L) 202

qEUBO: A Decision-Theoretic Acquisition Function for Preferential Bayesian Optimization

Preferential Bayesian optimization (PBO) is a framework for optimizing a decision maker's latent utility function using preference feedback. This work introduces the expected utility of the best option (qEUBO) as a novel acquisition function for PBO. When the decision maker's responses are noise-free, we show that qEUBO is one-step Bayes optimal and thus equivalent to the popular knowledge gradient acquisition function. We also show that qEUBO enjoys an additive constant approximation guarantee to the one-step Bayes-optimal policy when the decision maker's responses are corrupted by noise. We provide an extensive evaluation of qEUBO and demonstrate that it outperforms the state-of-the-art acquisition functions for PBO across many settings. Finally, we show that, under sufficient regularity conditions, qEUBO's Bayesian simple regret converges to zero at a rate $o(1/n)$ as the number of queries, $n$, goes to infinity. In contrast, we show that simple regret under qEI, a popular acquisition function for standard BO often used for PBO, can fail to converge to zero. Enjoying superior performance, simple computation, and a grounded decision-theoretic justification, qEUBO is a promising acquisition function for PBO.Comment: In Proceedings of the 26th International Conference on Artificial Intelligence and Statistics (AISTATS) 202

Human-in-the-Loop Optimization for Deep Stimulus Encoding in Visual Prostheses

Neuroprostheses show potential in restoring lost sensory function and enhancing human capabilities, but the sensations produced by current devices often seem unnatural or distorted. Exact placement of implants and differences in individual perception lead to significant variations in stimulus response, making personalized stimulus optimization a key challenge. Bayesian optimization could be used to optimize patient-specific stimulation parameters with limited noisy observations, but is not feasible for high-dimensional stimuli. Alternatively, deep learning models can optimize stimulus encoding strategies, but typically assume perfect knowledge of patient-specific variations. Here we propose a novel, practically feasible approach that overcomes both of these fundamental limitations. First, a deep encoder network is trained to produce optimal stimuli for any individual patient by inverting a forward model mapping electrical stimuli to visual percepts. Second, a preferential Bayesian optimization strategy utilizes this encoder to optimize patient-specific parameters for a new patient, using a minimal number of pairwise comparisons between candidate stimuli. We demonstrate the viability of this approach on a novel, state-of-the-art visual prosthesis model. We show that our approach quickly learns a personalized stimulus encoder, leads to dramatic improvements in the quality of restored vision, and is robust to noisy patient feedback and misspecifications in the underlying forward model. Overall, our results suggest that combining the strengths of deep learning and Bayesian optimization could significantly improve the perceptual experience of patients fitted with visual prostheses and may prove a viable solution for a range of neuroprosthetic technologies

BOgen: Generating Part-Level 3D Designs Based on User Intention Inference through Bayesian Optimization and Variational Autoencoder

Advancements in generative artificial intelligence (AI) have introduced various AI models capable of producing impressive visual design outputs. However, when it comes to AI models in the design process, prioritizing outputs that align with designers' needs over mere visual craftsmanship becomes even more crucial. Furthermore, designers often intricately combine parts of various designs to create novel designs. The ability to generate designs that align with the designers' intentions at the part level is pivotal for assisting designers. Hence, we introduced BOgen, which empowers designers to proactively generate and explore part-level designs through Bayesian optimization and variational autoencoders, thereby enhancing their overall user experience. We assessed BOgen's performance using a study involving 30 designers. The results revealed that, compared to the baseline, BOgen fulfilled the designer requirements for part recommendations and design exploration space guidance. BOgen assists designers in navigation and development, offering valuable design suggestions and fosters proactive design exploration and creation.Comment: 17 pages, 13 figure

Preferential Batch Bayesian Optimization

Most research in Bayesian optimization (BO) has focused on \emph{direct feedback} scenarios, where one has access to exact, or perturbed, values of some expensive-to-evaluate objective. This direction has been mainly driven by the use of \bo in machine learning hyper-parameter configuration problems. However, in domains such as modelling human preferences, A/B tests or recommender systems, there is a need of methods that are able to replace direct feedback with \emph{preferential feedback}, obtained via rankings or pairwise comparisons. In this work, we present Preferential Batch Bayesian Optimization (PBBO), a new framework that allows to find the optimum of a latent function of interest, given any type of parallel preferential feedback for a group of two or more points. We do so by using a Gaussian process model with a likelihood specially designed to enable parallel and efficient data collection mechanisms, which are key in modern machine learning. We show how the acquisitions developed under this framework generalize and augment previous approaches in Bayesian optimization, expanding the use of these techniques to a wider range of domains. An extensive simulation study shows the benefits of this approach, both with simulated functions and four real data sets