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04/24/2023
Causal fault localisation in dataflow systems
Dataflow computing was shown to bring significant benefits to multiple n...
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03/16/2023
Dataflow graphs as complete causal graphs
Component-based development is one of the core principles behind modern ...
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02/16/2023
Trieste: Efficiently Exploring The Depths of Black-box Functions with TensorFlow
We present Trieste, an open-source Python package for Bayesian optimizat...
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02/09/2023
Real-world Machine Learning Systems: A survey from a Data-Oriented Architecture Perspective
With the upsurge of interest in artificial intelligence machine learning...
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10/26/2022
Desiderata for next generation of ML model serving
Inference is a significant part of ML software infrastructure. Despite t...
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06/27/2022
A penalisation method for batch multi-objective Bayesian optimisation with application in heat exchanger design
We present HIghly Parallelisable Pareto Optimisation (HIPPO) – a batch a...
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04/27/2022
An Empirical Evaluation of Flow Based Programming in the Machine Learning Deployment Context
As use of data driven technologies spreads, software engineers are more ...
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10/25/2021
Emulation of physical processes with Emukit
Decision making in uncertain scenarios is an ubiquitous challenge in rea...
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08/09/2021
Exploring the potential of flow-based programming for machine learning deployment in comparison with service-oriented architectures
Despite huge successes reported by the field of machine learning, such a...
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12/31/2020
Good practices for Bayesian Optimization of high dimensional structured spaces
The increasing availability of structured but high dimensional data has ...
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11/18/2020
Challenges in Deploying Machine Learning: a Survey of Case Studies
In recent years, machine learning has received increased interest both a...
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05/24/2020
Causal Bayesian Optimization
This paper studies the problem of globally optimizing a variable of inte...
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05/26/2019
