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ORCIDAndrei Paleyes
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2020 – today
- 2024
[c7]Andrei Paleyes
, Han-Bo Li, Neil D. Lawrence:
Can causality accelerate experimentation in software systems? CAIN 2024: 280-281- [c6]Christian Cabrera, Andrei Paleyes, Neil D. Lawrence:
Self-sustaining Software Systems (S4): Towards Improved Interpretability and Adaptation. SATrends 2024: 5-9 - [c5]Christian Cabrera, Andrei Paleyes, Neil David Lawrence:
Self-sustaining Software Systems (S4): Towards Improved Interpretability and Adaptation. SATrends@ICSE 2024: 5-9 - [i15]Christian Cabrera, Andrei Paleyes, Neil D. Lawrence:
Self-sustaining Software Systems (S4): Towards Improved Interpretability and Adaptation. CoRR abs/2401.11370 (2024) - 2023
- [j2]Andrei Paleyes, Raoul-Gabriel Urma, Neil D. Lawrence:
Challenges in Deploying Machine Learning: A Survey of Case Studies. ACM Comput. Surv. 55(6): 114:1-114:29 (2023) - [c4]Andrei Paleyes, Siyuan Guo, Bernhard Schölkopf, Neil D. Lawrence:
Dataflow graphs as complete causal graphs. CAIN 2023: 7-12 - [c3]Andrei Paleyes, Neil David Lawrence:
Causal fault localisation in dataflow systems. EuroMLSys@EuroSys 2023: 140-147 - [i14]Christian Cabrera, Andrei Paleyes, Pierre Thodoroff, Neil D. Lawrence:
Real-world Machine Learning Systems: A survey from a Data-Oriented Architecture Perspective. CoRR abs/2302.04810 (2023) - [i13]Victor Picheny, Joel Berkeley, Henry B. Moss, Hrvoje Stojic, Uri Granta, Sebastian W. Ober, Artem Artemev, Khurram Ghani, Alexander Goodall, Andrei Paleyes, Sattar Vakili, Sergio Pascual-Diaz, Stratis Markou, Jixiang Qing, Nasrulloh R. B. S. Loka, Ivo Couckuyt:
Trieste: Efficiently Exploring The Depths of Black-box Functions with TensorFlow. CoRR abs/2302.08436 (2023) - [i12]Andrei Paleyes, Siyuan Guo, Bernhard Schölkopf, Neil D. Lawrence:
Dataflow graphs as complete causal graphs. CoRR abs/2303.09552 (2023) - [i11]Andrei Paleyes, Neil D. Lawrence:
Causal fault localisation in dataflow systems. CoRR abs/2304.11987 (2023) - [i10]Bogdan Ficiu, Neil D. Lawrence, Andrei Paleyes:
Automated discovery of trade-off between utility, privacy and fairness in machine learning models. CoRR abs/2311.15691 (2023) - 2022
- [c2]Andrei Paleyes, Christian Cabrera, Neil D. Lawrence:
An empirical evaluation of flow based programming in the machine learning deployment context. CAIN 2022: 54-64 - [i9]Andrei Paleyes, Christian Cabrera, Neil D. Lawrence:
An Empirical Evaluation of Flow Based Programming in the Machine Learning Deployment Context. CoRR abs/2204.12781 (2022) - [i8]Andrei Paleyes, Henry B. Moss, Victor Picheny, Piotr Zulawski, Felix Newman:
A penalisation method for batch multi-objective Bayesian optimisation with application in heat exchanger design. CoRR abs/2206.13326 (2022) - [i7]Sherif Akoush, Andrei Paleyes, Arnaud Van Looveren, Clive Cox:
Desiderata for next generation of ML model serving. CoRR abs/2210.14665 (2022) - 2021
- [i6]Andrei Paleyes, Christian Cabrera, Neil D. Lawrence:
Exploring the potential of flow-based programming for machine learning deployment in comparison with service-oriented architectures. CoRR abs/2108.04105 (2021) - [i5]Andrei Paleyes, Mark Pullin, Maren Mahsereci, Cliff McCollum, Neil D. Lawrence, Javier González:
Emulation of physical processes with Emukit. CoRR abs/2110.13293 (2021) - 2020
- [j1]Brendan Avent, Javier González, Tom Diethe, Andrei Paleyes, Borja Balle:
Automatic Discovery of Privacy-Utility Pareto Fronts. Proc. Priv. Enhancing Technol. 2020(4): 5-23 (2020) - [c1]Virginia Aglietti, Xiaoyu Lu, Andrei Paleyes, Javier González:
Causal Bayesian Optimization. AISTATS 2020: 3155-3164 - [i4]Virginia Aglietti, Xiaoyu Lu, Andrei Paleyes, Javier González:
Causal Bayesian Optimization. CoRR abs/2005.11741 (2020) - [i3]Andrei Paleyes, Raoul-Gabriel Urma, Neil D. Lawrence:
Challenges in Deploying Machine Learning: a Survey of Case Studies. CoRR abs/2011.09926 (2020) - [i2]Eero Siivola, Javier González, Andrei Paleyes, Aki Vehtari:
Good practices for Bayesian Optimization of high dimensional structured spaces. CoRR abs/2012.15471 (2020)
2010 – 2019
- 2019
- [i1]Brendan Avent, Javier González, Tom Diethe, Andrei Paleyes, Borja Balle:
Automatic Discovery of Privacy-Utility Pareto Fronts. CoRR abs/1905.10862 (2019)
Coauthor Index
aka: Neil David Lawrence
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