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Learning a strategy for adapting a program analysis via bayesian optimisation
2015
SIGPLAN notices
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We present a method for learning a good parameter for such a strategy from an existing codebase via Bayesian optimisation. The learnt strategy is then used for new, unseen programs. ...
The experimental results demonstrate that using Bayesian optimisation is crucial for learning from an existing codebase. ...
Learning via Bayesian optimisation We present our approach for learning a parameter of the adaptation strategy. ...
doi:10.1145/2858965.2814309
fatcat:osfeez6qyzarrcpueopxq4sqpy
Learning a strategy for adapting a program analysis via bayesian optimisation
2015
Proceedings of the 2015 ACM SIGPLAN International Conference on Object-Oriented Programming, Systems, Languages, and Applications - OOPSLA 2015
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We present a method for learning a good parameter for such a strategy from an existing codebase via Bayesian optimisation. The learnt strategy is then used for new, unseen programs. ...
The experimental results demonstrate that using Bayesian optimisation is crucial for learning from an existing codebase. ...
Learning via Bayesian optimisation We present our approach for learning a parameter of the adaptation strategy. ...
doi:10.1145/2814270.2814309
dblp:conf/oopsla/OhYY15
fatcat:x5yncypgofb35cw6kjlibwxjoy
Myopic Bayesian Design of Experiments via Posterior Sampling and Probabilistic Programming
[article]
2018
arXiv
pre-print
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We design a new myopic strategy for a wide class of sequential design of experiment (DOE) problems, where the goal is to collect data in order to to fulfil a certain problem specific goal. ...
approximate Bayesian inference to address a broad set of problems. ...
) program. ...
arXiv:1805.09964v1
fatcat:bzfexbjdavfmncpx75uc5psisi
Myopic Posterior Sampling for Adaptive Goal Oriented Design of Experiments
2019
International Conference on Machine Learning
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Bayesian methods for adaptive decision-making, such as Bayesian optimisation, active learning, and active search have seen great success in relevant applications. ...
In this work, we design a new myopic strategy for a wide class of adaptive design of experiment (DOE) problems, where we wish to collect data in order to fulfil a given goal. ...
KK and AK would like to thank La Flamenca, Lanzarote for their delicious vegetarian paellas, which fueled the initial ideas for our theoretical analysis. ...
dblp:conf/icml/KandasamyNZKSP19
fatcat:gfrg5y56yfb2bp2wfskceokrk4
Bayesian Optimisation for Adaptive Experimental Design: A review
2020
IEEE Access
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Bayesian optimisation is a statistical method that efficiently models and optimises expensive "black-box" functions. ...
INDEX TERMS Bayesian methods, design for experiments, design optimization, machine learning algorithms. 13938 VOLUME 8, 2020 ...
In contrast, Bayesian optimisation uses a model based approach with an adaptive sampling strategy to minimise the number of function evaluations. ...
doi:10.1109/access.2020.2966228
fatcat:f4vz4jvjczc4dlg2twoqlrbtiy
Machine Learning for Combinatorial Optimisation of Partially-Specified Problems: Regret Minimisation as a Unifying Lens
[article]
2022
arXiv
pre-print
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The challenge is to learn them from available data, while taking into account a set of hard constraints that a solution must satisfy, and that solving the optimisation problem (esp. during learning) is ...
This paper overviews four seemingly unrelated approaches, that can each be viewed as learning the objective function of a hard combinatorial optimisation problem: 1) surrogate-based optimisation, 2) empirical ...
Acknowledgements This research was partially supported by TAILOR, a project funded by EU Horizon 2020 research and innovation programme under grant number 952215. ...
arXiv:2205.10157v1
fatcat:eshydnjhb5hp3gi27doojax3dq
A Concise Review of Energy Management Strategies for Hybrid Energy Storage Systems
2022
European Journal of Engineering and Technology Research
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The Reinforcement learning-based algorithm which uses an agent-based approach to optimally control the system offers an optimal solution for energy management. ...
The energy management strategies were grouped into forecast/historical, heuristic logic, ANN-fuzzy logic, and reinforcement learning (machine learning) based methods. ...
for training by Support vector regression (SVR), a Bayesian machine learning method. ...
doi:10.24018/ejeng.2022.7.3.2815
fatcat:hbjv3xf2yzdqberuik5cqcahry
Theory of Randomized Optimization Heuristics (Dagstuhl Seminar 17191)
2017
Dagstuhl Reports
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breakout sessions, and discussions at the Dagstuhl Seminar 17191 on Theory of Randomized Optimization Heuristics, held during the week from May 08 until May 12, 2017, in Schloss Dagstuhl -Leibniz Center for ...
The meeting is the successor of the "Theory of Evolutionary Algorithm" seminar series, where the change in the title reflects the development of the research field toward a broader range of heuristics. ...
M. and Yehudayoff, A. On the statistical learning ability of evolution strategies. ...
doi:10.4230/dagrep.7.5.22
dblp:journals/dagstuhl-reports/DoerrIT017
fatcat:guma4eanyne6vlkwexkevs4v6i
Bayesian statistical learning for big data biology
2019
Biophysical Reviews
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Bayesian statistical learning provides a coherent probabilistic framework for modelling uncertainty in systems. ...
We then describe the use of Bayesian learning in single-cell biology for the analysis of high-dimensional, large data sets. ...
Acknowledgements CY is supported by a UK Medical Research Council Research Grant (Ref: MR/P02646X/1) and by The Alan Turing Institute under the EPSRC grant EP/N510129/1. ...
doi:10.1007/s12551-019-00499-1
pmid:30729409
pmcid:PMC6381359
fatcat:26l5tkuawrarbni4vc6srasvd4
A review on the self and dual interactions between machine learning and optimisation
2019
Progress in Artificial Intelligence
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This study focuses on such interactions aiming at (1) presenting a broad overview of the studies on self and dual interactions between machine learning and optimisation; (2) providing a useful tutorial ...
The techniques in the former area aim to learn knowledge from data or experience, while the techniques from the latter search for the best option or solution to a given problem. ...
Bayesian optimisation is a sequential design strategy for global optimisation of black-box functions that does not require derivatives [114] . ...
doi:10.1007/s13748-019-00185-z
fatcat:zr5dsschzzddzgfvwu4sl2jeou
Data-Centric Engineering: integrating simulation, machine learning and statistics. Challenges and Opportunities
[article]
2021
arXiv
pre-print
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New hybrid, data-centric engineering approaches, leveraging the best of both worlds and integrating both simulations and data, are emerging as a powerful tool with a transformative impact on the physical ...
We review the key research trends and application scenarios in the emerging field of integrating simulations, machine learning, and statistics. ...
UK, through the Programme Grant PREMIERE (EP/T000414/1), as well as funding through the Wave 1 of The UKRI Strategic Priorities Fund under the EPSRC Grant EP/T001569/1, particularly the Digital Twins for ...
arXiv:2111.06223v2
fatcat:ejwhon6envhandje4twvatgliq
Bayesian Optimisation for Neuroimaging Pre-processing in Brain Age Prediction
[article]
2017
bioRxiv
pre-print
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Bayesian optimisation was used to identify optimal voxel size and smoothing kernel size for each task. ...
When assessing generalisability, best performance was achieved when applying the entire Bayesian optimisation framework to the new dataset, out-performing the parameters optimised for the initial training ...
Here, we outline a principled Bayesian optimisation 101 strategy for identifying optimal values for pre-processing parameters in neuroimaging analysis, 102 implementing sub-sampling to avoid bias. ...
doi:10.1101/201061
fatcat:tlk4j6kgzfhive6xumy4moe5qy
A Review of Safe Online Learning for Nonlinear Control Systems
2021
2021 International Conference on Unmanned Aircraft Systems (ICUAS)
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Learning for autonomous dynamic control systems that can adapt to unforeseen environmental changes are of great interest but the realisation of a practical and safe online learning algorithm is incredibly ...
We categorise a non-exhaustive list of salient techniques, with a focus on traditional control theory as opposed to reinforcement learning and approximate dynamic programming. ...
The authors would also like to thank the following researchers for their kind assistance. Sumeet Singh, Ian Manchester and Johan Löfberg. ...
doi:10.1109/icuas51884.2021.9476765
fatcat:6uf4ad73ynccbgtbo7sqkn3qty
Chapter Eleven Modelling and Monitoring Environmental Outcomes in Adaptive Management
[chapter]
2008
Developments in Integrated Environmental Assessment
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for each other. ...
The method will be to compare the histories, strengths and limitations of AM, control engineering and Bayesian analysis, which have superficial similarities, significant differences and perhaps lessons ...
Bayesian Analysis provides a rigorous and logical learning model for adaptive management under uncertainty. ...
doi:10.1016/s1574-101x(08)00611-x
fatcat:ogtgqb4ffbdvnm3le32th7dyli
Evolutionary Algorithms in Management Applications
1997
Journal of the Operational Research Society
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specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. ...
Heidelberg 1995 Softcover reprint of the hardcover 1 st edition 1995 The use of general descriptive names. registered names. trademarks. etc. in this publication does not imply. even in the absence of a ...
They employ a simple Evolution Strategy as the basic optimisation technique. ...
doi:10.1057/palgrave.jors.2600361
fatcat:vwgo7rc5obeghn42xgdgkbtdlm
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