| Challenges in deploying machine learning: a survey of case studies A Paleyes, RG Urma, ND Lawrence ACM Computing Surveys (CSUR), 2020 | 339 | 2020 |
| Emulation of physical processes with emukit A Paleyes, M Pullin, M Mahsereci, C McCollum, N Lawrence, J González Second workshop on machine learning and the physical sciences, NeurIPS 2019, 2019 | 113 | 2019 |
| Causal bayesian optimization V Aglietti, X Lu, A Paleyes, J González International Conference on Artificial Intelligence and Statistics, 3155-3164, 2020 | 42 | 2020 |
| Automatic discovery of privacy-utility pareto fronts B Avent, J Gonzalez, T Diethe, A Paleyes, B Balle Privacy Enhancing Technologies Symposium, PETS 2020, 2019 | 30 | 2019 |
| Good practices for Bayesian optimization of high dimensional structured spaces E Siivola, A Paleyes, J González, A Vehtari Applied AI Letters 2 (2), e24, 2021 | 28* | 2021 |
| Trieste: Efficiently exploring the depths of black-box functions with TensorFlow V Picheny, J Berkeley, HB Moss, H Stojic, U Granta, SW Ober, A Artemev, ... arXiv preprint arXiv:2302.08436, 2023 | 22* | 2023 |
| Effectiveness and resource requirements of test, trace and isolate strategies for COVID in the UK B He, S Zaidi, B Elesedy, M Hutchinson, A Paleyes, G Harling, ... Royal Society open science 8 (3), 201491, 2021 | 11* | 2021 |
| Towards better data discovery and collection with flow-based programming A Paleyes, C Cabrera, ND Lawrence Data-centric AI workshop, NeurIPS 2021, 2021 | 8* | 2021 |
| DELVE global COVID-19 dataset A Bhoopchand, A Paleyes, K Donkers, N Tomasev, U Paquet Published June 2 2020, 2020 | 8* | 2020 |
| Dataflow graphs as complete causal graphs A Paleyes, S Guo, B Scholkopf, ND Lawrence 2023 IEEE/ACM 2nd International Conference on AI Engineering–Software …, 2023 | 5* | 2023 |
| Desiderata for next generation of ML model serving S Akoush, A Paleyes, A Van Looveren, C Cox Workshop on Challenges in Deploying and Monitoring Machine Learning Systems …, 2022 | 5 | 2022 |
| A penalisation method for batch multi-objective Bayesian optimisation with application in heat exchanger design A Paleyes, HB Moss, V Picheny, P Zulawski, F Newman Adaptive Experimental Design and Active Learning in the Real World Workshop …, 2022 | 5 | 2022 |
| An empirical evaluation of flow based programming in the machine learning deployment context A Paleyes, C Cabrera, ND Lawrence Proceedings of the 1st International Conference on AI Engineering: Software …, 2022 | 5 | 2022 |
| Real-world Machine Learning Systems: A survey from a Data-Oriented Architecture Perspective C Cabrera, A Paleyes, P Thodoroff, ND Lawrence arXiv preprint arXiv:2302.04810, 2023 | 3 | 2023 |
| Automated discovery of trade-off between utility, privacy and fairness in machine learning models B Ficiu, ND Lawrence, A Paleyes arXiv preprint arXiv:2311.15691, 2023 | 2 | 2023 |
| Causal fault localisation in dataflow systems A Paleyes, ND Lawrence Proceedings of the 3rd Workshop on Machine Learning and Systems, 140-147, 2023 | 2 | 2023 |
| Emukit: A Python toolkit for decision making under uncertainty A Paleyes, M Mahsereci, ND Lawrence Proceedings of the Python in Science Conference, 2023 | 2 | 2023 |
| Self-sustaining Software Systems (S4): Towards Improved Interpretability and Adaptation C Cabrera, A Paleyes, ND Lawrence arXiv preprint arXiv:2401.11370, 2024 | | 2024 |
Neil D. LawrenceUniversity of CambridgeVerified email at cl.cam.ac.uk
