Andrei Paleyes

According to our database, Andrei Paleyes authored at least 23 papers between 2020 and 2026.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2026

Machine Learning Systems: A Survey from a Data-Oriented Perspective.

Christian Cabrera, Andrei Paleyes, Pierre Thodoroff, Neil D. Lawrence

ACM Comput. Surv., April, 2026

Optimising for Energy Efficiency and Performance in Machine Learning.

Emile Dos Santos Ferreira, Andrei Paleyes, Neil D. Lawrence

CoRR, January, 2026

2025

Surrogate-Based Differentiable Pipeline for Shape Optimization.

Andrin Rehmann, Nolan Black, Josiah Bjorgaard, Alessandro Angioi, Andrei Paleyes, Niklas Heim, Dion Häfner, Alexander Lavin

CoRR, November, 2025

Prompt Variability Effects On LLM Code Generation.

Andrei Paleyes, Radzim Sendyka, Diana Robinson, Christian Cabrera, Neil D. Lawrence

CoRR, June, 2025

LLM Performance for Code Generation on Noisy Tasks.

Radzim Sendyka, Christian Cabrera, Andrei Paleyes, Diana Robinson, Neil D. Lawrence

CoRR, May, 2025

Pipeline-level differentiable programming for the real world.

Andrin Rehmann, Dion Häfner, Alessandro Angioi, Yongquan Qu, Andrei Paleyes

Proceedings of the 24th Python in Science Conference, 2025

2024

Self-sustaining Software Systems (S4): Towards Improved Interpretability and Adaptation.

Christian Cabrera, Andrei Paleyes, Neil David Lawrence

Proceedings of the 1st International Workshop on New Trends in Software Architecture, 2024

Can causality accelerate experimentation in software systems?

Andrei Paleyes, Han-Bo Li, Neil D. Lawrence

Proceedings of the IEEE/ACM 3rd International Conference on AI Engineering, 2024

2023

Challenges in Deploying Machine Learning: A Survey of Case Studies.

Andrei Paleyes, Raoul-Gabriel Urma, Neil D. Lawrence

ACM Comput. Surv., 2023

Trieste: Efficiently Exploring The Depths of Black-box Functions with TensorFlow.

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

CoRR, 2023

Real-world Machine Learning Systems: A survey from a Data-Oriented Architecture Perspective.

Christian Cabrera, Andrei Paleyes, Pierre Thodoroff, Neil D. Lawrence

CoRR, 2023

Emukit: A Python toolkit for decision making under uncertainty.

Andrei Paleyes, Maren Mahsereci, Neil D. Lawrence

Proceedings of the 22nd Python in Science Conference, 2023

Automated Discovery of Trade-Off Between Utility, Privacy and Fairness in Machine Learning Models.

Bogdan Ficiu, Neil D. Lawrence, Andrei Paleyes

Proceedings of the Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2023

Causal fault localisation in dataflow systems.

Andrei Paleyes, Neil David Lawrence

Proceedings of the 3rd Workshop on Machine Learning and Systems, 2023

Dataflow graphs as complete causal graphs.

Andrei Paleyes, Siyuan Guo, Bernhard Schölkopf, Neil D. Lawrence

Proceedings of the 2nd IEEE/ACM International Conference on AI Engineering, 2023

2022

Desiderata for next generation of ML model serving.

Sherif Akoush, Andrei Paleyes, Arnaud Van Looveren, Clive Cox

CoRR, 2022

A penalisation method for batch multi-objective Bayesian optimisation with application in heat exchanger design.

Andrei Paleyes, Henry B. Moss, Victor Picheny, Piotr Zulawski, Felix Newman

CoRR, 2022

An empirical evaluation of flow based programming in the machine learning deployment context.

Andrei Paleyes, Christian Cabrera, Neil D. Lawrence

Proceedings of the 1st International Conference on AI Engineering: Software Engineering for AI, 2022

2021

Emulation of physical processes with Emukit.

Andrei Paleyes, Mark Pullin, Maren Mahsereci, Cliff McCollum, Neil D. Lawrence, Javier González

CoRR, 2021

Exploring the potential of flow-based programming for machine learning deployment in comparison with service-oriented architectures.

Andrei Paleyes, Christian Cabrera, Neil D. Lawrence

CoRR, 2021

2020

Automatic Discovery of Privacy-Utility Pareto Fronts.

Brendan Avent, Javier González, Tom Diethe, Andrei Paleyes, Borja Balle

Proc. Priv. Enhancing Technol., 2020

Good practices for Bayesian Optimization of high dimensional structured spaces.

Eero Siivola, Javier González, Andrei Paleyes, Aki Vehtari

CoRR, 2020

Causal Bayesian Optimization.

Virginia Aglietti, Xiaoyu Lu, Andrei Paleyes, Javier González

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020