Epsilon-Greedy Thompson Sampling to Bayesian Optimization

Thompson sampling (TS) serves as a solution for addressing the exploitation-exploration dilemma in Bayesian optimization (BO). While it prioritizes exploration by randomly generating and maximizing sample paths of Gaussian process (GP) posteriors, TS weakly manages its exploitation by gathering information about the true objective function after each exploration is performed. In this study, we incorporate the epsilon-greedy ($\varepsilon$-greedy) policy, a well-established selection strategy in reinforcement learning, into TS to improve its exploitation. We first delineate two extremes of TS applied for BO, namely the generic TS and a sample-average TS. The former and latter promote exploration and exploitation, respectively. We then use $\varepsilon$-greedy policy to randomly switch between the two extremes. A small value of $\varepsilon \in (0,1)$ prioritizes exploitation, and vice versa. We empirically show that $\varepsilon$-greedy TS with an appropriate $\varepsilon$ is better than one of its two extremes and competes with the other.

Multi-fidelity Bayesian Optimization in Engineering Design

Resided at the intersection of multi-fidelity optimization (MFO) and Bayesian optimization (BO), MF BO has found a niche in solving expensive engineering design optimization problems, thanks to its advantages in incorporating physical and mathematical understandings of the problems, saving resources, addressing exploitation-exploration trade-off, considering uncertainty, and processing parallel computing. The increasing number of works dedicated to MF BO suggests the need for a comprehensive review of this advanced optimization technique. In this paper, we survey recent developments of two essential ingredients of MF BO: Gaussian process (GP) based MF surrogates and acquisition functions. We first categorize the existing MF modeling methods and MFO strategies to locate MF BO in a large family of surrogate-based optimization and MFO algorithms. We then exploit the common properties shared between the methods from each ingredient of MF BO to describe important GP-based MF surrogate models and review various acquisition functions. By doing so, we expect to provide a structured understanding of MF BO. Finally, we attempt to reveal important aspects that require further research for applications of MF BO in solving intricate yet important design optimization problems, including constrained optimization, high-dimensional optimization, optimization under uncertainty, and multi-objective optimization.

Ensemble Learning of Myocardial Displacements for Myocardial Infarction Detection in Echocardiography

Early detection and localization of myocardial infarction (MI) can reduce the severity of cardiac damage through timely treatment interventions. In recent years, deep learning techniques have shown promise for detecting MI in echocardiographic images. However, there has been no examination of how segmentation accuracy affects MI classification performance and the potential benefits of using ensemble learning approaches. Our study investigates this relationship and introduces a robust method that combines features from multiple segmentation models to improve MI classification performance by leveraging ensemble learning. Our method combines myocardial segment displacement features from multiple segmentation models, which are then input into a typical classifier to estimate the risk of MI. We validated the proposed approach on two datasets: the public HMC-QU dataset (109 echocardiograms) for training and validation, and an E-Hospital dataset (60 echocardiograms) from a local clinical site in Vietnam for independent testing. Model performance was evaluated based on accuracy, sensitivity, and specificity. The proposed approach demonstrated excellent performance in detecting MI. The results showed that the proposed approach outperformed the state-of-the-art feature-based method. Further research is necessary to determine its potential use in clinical settings as a tool to assist cardiologists and technicians with objective assessments and reduce dependence on operator subjectivity. Our research codes are available on GitHub at https://github.com/vinuni-vishc/mi-detection-echo.