Self-Correcting Bayesian Optimization through Bayesian Active Learning

Gaussian processes are cemented as the model of choice in Bayesian optimization and active learning. Yet, they are severely dependent on cleverly chosen hyperparameters to reach their full potential, and little effort is devoted to finding the right hyperparameters in the literature. We demonstrate the impact of selecting good hyperparameters for GPs and present two acquisition functions that explicitly prioritize this goal. Statistical distance-based Active Learning (SAL) considers the average disagreement among samples from the posterior, as measured by a statistical distance. It is shown to outperform the state-of-the-art in Bayesian active learning on a number of test functions. We then introduce Self-Correcting Bayesian Optimization (SCoreBO), which extends SAL to perform Bayesian optimization and active hyperparameter learning simultaneously. SCoreBO learns the model hyperparameters at improved rates compared to vanilla BO, while outperforming the latest Bayesian optimization methods on traditional benchmarks. Moreover, the importance of self-correction is demonstrated on an array of exotic Bayesian optimization task

Learning Skill-based Industrial Robot Tasks with User Priors

Robot skills systems are meant to reduce robot setup time for new manufacturing tasks. Yet, for dexterous, contact-rich tasks, it is often difficult to find the right skill parameters. One strategy is to learn these parameters by allowing the robot system to learn directly on the task. For a learning problem, a robot operator can typically specify the type and range of values of the parameters. Nevertheless, given their prior experience, robot operators should be able to help the learning process further by providing educated guesses about where in the parameter space potential optimal solutions could be found. Interestingly, such prior knowledge is not exploited in current robot learning frameworks. We introduce an approach that combines user priors and Bayesian optimization to allow fast optimization of robot industrial tasks at robot deployment time. We evaluate our method on three tasks that are learned in simulation as well as on two tasks that are learned directly on a real robot system. Additionally, we transfer knowledge from the corresponding simulation tasks by automatically constructing priors from well-performing configurations for learning on the real system. To handle potentially contradicting task objectives, the tasks are modeled as multi-objective problems. Our results show that operator priors, both user-specified and transferred, vastly accelerate the discovery of rich Pareto fronts, and typically produce final performance far superior to proposed baselines.Comment: 8 pages, 6 figures, accepted at 2022 IEEE International Conference on Automation Science and Engineering (CASE

PriorBand: Practical Hyperparameter Optimization in the Age of Deep Learning

Hyperparameters of Deep Learning (DL) pipelines are crucial for their downstream performance. While a large number of methods for Hyperparameter Optimization (HPO) have been developed, their incurred costs are often untenable for modern DL. Consequently, manual experimentation is still the most prevalent approach to optimize hyperparameters, relying on the researcher's intuition, domain knowledge, and cheap preliminary explorations. To resolve this misalignment between HPO algorithms and DL researchers, we propose PriorBand, an HPO algorithm tailored to DL, able to utilize both expert beliefs and cheap proxy tasks. Empirically, we demonstrate PriorBand's efficiency across a range of DL benchmarks and show its gains under informative expert input and robustness against poor expert belief

Oxygen provision to severely ill COVID-19 patients at the peak of the 2020 pandemic in a Swedish district hospital.

Oxygen is a low-cost and life-saving therapy for patients with COVID-19. Yet, it is a limited resource in many hospitals in low income countries and in the 2020 pandemic even hospitals in richer countries reported oxygen shortages. An accurate understanding of oxygen requirements is needed for capacity planning. The World Health Organization estimates the average flow-rate of oxygen to severe COVID-19-patients to be 10 l/min. However, there is a lack of empirical data about the oxygen provision to patients. This study aimed to estimate the oxygen provision to COVID-19 patients with severe disease in a Swedish district hospital. A retrospective, medical records-based cohort study was conducted in March to May 2020 in a Swedish district hospital. All adult patients with severe COVID-19 -those who received oxygen in the ward and had no ICU-admission during their hospital stay-were included. Data were collected on the oxygen flow-rates provided to the patients throughout their hospital stay, and summary measures of oxygen provision calculated. One-hundred and twenty-six patients were included, median age was 70 years and 43% were female. On admission, 27% had a peripheral oxygen saturation of ≤91% and 54% had a respiratory rate of ≥25/min. The mean oxygen flow-rate to patients while receiving oxygen therapy was 3.0 l/min (SD 2.9) and the mean total volume of oxygen provided per patient admission was 16,000 l (SD 23,000). In conclusion, the provision of oxygen to severely ill COVID-19-patients was lower than previously estimated. Further research is required before global estimates are adjusted

Joint Entropy Search for Maximally-Informed Bayesian Optimization

Information-theoretic Bayesian optimization techniques have become popular for optimizing expensive-to-evaluate black-box functions due to their non-myopic qualities. Entropy Search and Predictive Entropy Search both consider the entropy over the optimum in the input space, while the recent Max-value Entropy Search considers the entropy over the optimal value in the output space. We propose Joint Entropy Search (JES), a novel information-theoretic acquisition function that considers an entirely new quantity, namely the entropy over the joint optimal probability density over both input and output space. To incorporate this information, we consider the reduction in entropy from conditioning on fantasized optimal input/output pairs. The resulting approach primarily relies on standard GP machinery and removes complex approximations typically associated with information-theoretic methods. With minimal computational overhead, JES shows superior decision-making, and yields state-of-the-art performance for information-theoretic approaches across a wide suite of tasks. As a light-weight approach with superior results, JES provides a new go-to acquisition function for Bayesian optimization

Learning Skill-based Industrial Robot Tasks with User Priors

Robot skills systems are meant to reduce robot setup time for new manufacturing tasks. Yet, for dexterous, contact-rich tasks, it is often difficult to find the right skill parameters. One strategy is to learn these parameters by allowing the robot system to learn directly on the task. For a learning problem, a robot operator can typically specify the type and range of values of the parameters. Nevertheless, given their prior experience, robot operators should be able to help the learning process further by providing educated guesses about where in the parameter space potential optimal solutions could be found. Interestingly, such prior knowledge is not exploited in current robot learning frameworks. We introduce an approach that combines user priors and Bayesian optimization to allow fast optimization of robot industrial tasks at robot deployment time. We evaluate our method on three tasks that are learned in simulation as well as on two tasks that are learned directly on a real robot system. Additionally, we transfer knowledge from the corresponding simulation tasks by automatically constructing priors from well-performing configurations for learning on the real system. To handle potentially contradicting task objectives, the tasks are modeled as multi-objective problems. Our results show that operator priors, both user-specified and transferred, vastly accelerate the discovery of rich Pareto fronts, and typically produce final performance far superior to proposed baselines

πBO: Augmenting Acquisition Functions with User Beliefs for Bayesian Optimization

Bayesian optimization (BO) has become an established framework and popular tool for hyperparameter optimization (HPO) of machine learning (ML) algorithms. While known for its sample-efficiency, vanilla BO can not utilize readily available prior beliefs the practitioner has on the potential location of the optimum. Thus, BO disregards a valuable source of information, reducing its appeal to ML practitioners. To address this issue, we propose PiBO, an acquisition function generalization which incorporates prior beliefs about the location of the optimum in the form of a probability distribution, provided by the user. In contrast to previous approaches, PiBO is conceptually simple and can easily be integrated with existing libraries and many acquisition functions. We provide regret bounds when PiBO is applied to the common Expected Improvement acquisition function and prove convergence at regular rates independently of the prior. Further, our experiments show that BO outperforms competing approaches across a wide suite of benchmarks and prior characteristics. We also demonstrate that PiBO improves on the state-of-the-art performance for a popular deep learning task, with a 12.5 time-to-accuracy speedup over prominent BO approaches

The global need for essential emergency and critical care

Critical illness results in millions of deaths each year. Care for those with critical illness is often neglected due to a lack of prioritisation, co-ordination, and coverage of timely identification and basic life-saving treatments. To improve care, we propose a new focus on essential emergency and critical care (EECC)care that all critically ill patients should receive in all hospitals in the world. Essential emergency and critical care should be part of universal health coverage, is appropriate for all countries in the world, and is intended for patients irrespective of age, gender, underlying diagnosis, medical specialty, or location in the hospital. Essential emergency and critical care is pragmatic and low-cost and has the potential to improve care and substantially reduce preventable mortality

Vital Signs Directed Therapy for the Critically Ill : Improved Adherence to the Treatment Protocol Two Years after Implementation in an Intensive Care Unit in Tanzania

Treating deranged vital signs is a mainstay of critical care throughout the world. In an ICU in a university hospital in Tanzania, the implementation of the Vital Signs Directed Therapy Protocol in 2014 led to an increase in acute treatments for deranged vital signs. The mortality rate for hypotensive patients decreased from 92% to 69%. In this study, the aim was to investigate the sustainability of the implementation two years later. An observational, patient-record-based study was conducted in the ICU in August 2016. Data on deranged vital signs and acute treatments were extracted from the patients' charts. Adherence to the protocol, defined as an acute treatment in the same or subsequent hour following a deranged vital sign, was calculated and compared with before and immediately after implementation. Two-hundred and eighty-nine deranged vital signs were included. Adherence was 29.8% two years after implementation, compared with 16.6% (p<0.001) immediately after implementation and 2.9% (p<0.001) before implementation. Consequently, the implementation of the Vital Signs Directed Therapy Protocol appears to have led to a sustainable increase in the treatment of deranged vital signs. The protocol may have potential to improve patient safety in other settings where critically ill patients are managed