Tackling climate change with machine learning
Climate change is one of the greatest challenges facing humanity, and we, as machine
learning (ML) experts, may wonder how we can help. Here we describe how ML can be a …
learning (ML) experts, may wonder how we can help. Here we describe how ML can be a …
Bayesian optimization for adaptive experimental design: A review
Bayesian optimisation is a statistical method that efficiently models and optimises expensive
“black-box” functions. This review considers the application of Bayesian optimisation to …
“black-box” functions. This review considers the application of Bayesian optimisation to …
Machine learning for sustainable energy systems
In recent years, machine learning has proven to be a powerful tool for deriving insights from
data. In this review, we describe ways in which machine learning has been leveraged to …
data. In this review, we describe ways in which machine learning has been leveraged to …
Reinforcement learning-trained optimisers and Bayesian optimisation for online particle accelerator tuning
Online tuning of particle accelerators is a complex optimisation problem that continues to
require manual intervention by experienced human operators. Autonomous tuning is a …
require manual intervention by experienced human operators. Autonomous tuning is a …
Computational sustainability: Computing for a better world and a sustainable future
Computational sustainability: computing for a better world and a sustainable future Page 1 56
COMMUNICATIONS OF THE ACM | SEPTEMBER 2019 | VOL. 62 | NO. 9 Computational …
COMMUNICATIONS OF THE ACM | SEPTEMBER 2019 | VOL. 62 | NO. 9 Computational …
Virtual vs. real: Trading off simulations and physical experiments in reinforcement learning with Bayesian optimization
In practice, the parameters of control policies are often tuned manually. This is time-
consuming and frustrating. Reinforcement learning is a promising alternative that aims to …
consuming and frustrating. Reinforcement learning is a promising alternative that aims to …
Safe contextual Bayesian optimization for sustainable room temperature PID control tuning
We tune one of the most common heating, ventilation, and air conditioning (HVAC) control
loops, namely the temperature control of a room. For economical and environmental …
loops, namely the temperature control of a room. For economical and environmental …
No-regret Bayesian optimization with unknown hyperparameters
Bayesian optimization (BO) based on Gaussian process models is a powerful paradigm to
optimize black-box functions that are expensive to evaluate. While several BO algorithms …
optimize black-box functions that are expensive to evaluate. While several BO algorithms …
[HTML][HTML] Multi-objective optimization of environmental tax for mitigating air pollution and greenhouse gas
Government macro-control through various policies is an important way to mitigate air
pollution and greenhouse gases. Therefore, environmental tax is used worldwide as an …
pollution and greenhouse gases. Therefore, environmental tax is used worldwide as an …
Learning to do or learning while doing: Reinforcement learning and bayesian optimisation for online continuous tuning
Online tuning of real-world plants is a complex optimisation problem that continues to
require manual intervention by experienced human operators. Autonomous tuning is a …
require manual intervention by experienced human operators. Autonomous tuning is a …