625 citations · 721 across the 15 of their papers we have counts for
5 papers · 1 filter
Automated discovery of trade-off between utility, privacy and fairness in machine learning models
Bogdan Ficiu, Neil D. Lawrence, Andrei Paleyes
Machine learning models are deployed as a central component in decision making and policy operations with direct impact on individuals' lives. In order to act ethically and comply…
Causal fault localisation in dataflow systems
Andrei Paleyes, Neil D. Lawrence
Dataflow computing was shown to bring significant benefits to multiple niches of systems engineering and has the potential to become a general-purpose paradigm of choice for data-d…
Dataflow graphs as complete causal graphs
Andrei Paleyes, Siyuan Guo, Bernhard Schölkopf +1
Component-based development is one of the core principles behind modern software engineering practices. Understanding of causal relationships between components of a software syste…
Trieste: Efficiently Exploring The Depths of Black-box Functions with TensorFlow
Victor Picheny, Joel Berkeley, Henry B. Moss +13
We present Trieste, an open-source Python package for Bayesian optimization and active learning benefiting from the scalability and efficiency of TensorFlow. Our library enables th…
Machine Learning Systems: A Survey from a Data-Oriented Perspective
Christian Cabrera, Andrei Paleyes, Pierre Thodoroff +1
Engineers are deploying ML models as parts of real-world systems with the upsurge of AI technologies. Real-world environments challenge the deployment of such systems because these…