Andrei Paleyes

32 papers A 1B 3Journal 22Unranked 6

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Machine Learning Systems: A Survey from a Data-Oriented Perspective. ACM Comput. Surv. Christian Cabrera, Andrei Paleyes, Pierre Thodoroff, Neil D. Lawrence
2026	J	`jnl`	Optimising for Energy Efficiency and Performance in Machine Learning. CoRR Emile Dos Santos Ferreira, Andrei Paleyes, Neil D. Lawrence
2025	J	`jnl`	LLM Performance for Code Generation on Noisy Tasks. CoRR Radzim Sendyka, Christian Cabrera, Andrei Paleyes, Diana Robinson, Neil D. Lawrence
2025	—	`conf`	Pipeline-level differentiable programming for the real world. SciPy Andrin Rehmann, Dion Häfner, Alessandro Angioi, Yongquan Qu, Andrei Paleyes
2025	J	`jnl`	Prompt Variability Effects On LLM Code Generation. CoRR Andrei Paleyes, Radzim Sendyka, Diana Robinson, Christian Cabrera, Neil D. Lawrence
2025	J	`jnl`	Surrogate-Based Differentiable Pipeline for Shape Optimization. CoRR Andrin Rehmann, Nolan Black, Josiah Bjorgaard, Alessandro Angioi, Andrei Paleyes, Niklas Heim, Dion Häfner, Alexander Lavin
2024	B	`conf`	Can causality accelerate experimentation in software systems? CAIN Andrei Paleyes, Han-Bo Li, Neil D. Lawrence
2024	—	`conf`	Self-sustaining Software Systems (S4): Towards Improved Interpretability and Adaptation. SATrends Christian Cabrera, Andrei Paleyes, Neil D. Lawrence
2024	—	`conf`	Self-sustaining Software Systems (S4): Towards Improved Interpretability and Adaptation. SATrends@ICSE Christian Cabrera, Andrei Paleyes, Neil David Lawrence
2024	J	`jnl`	Self-sustaining Software Systems (S4): Towards Improved Interpretability and Adaptation. CoRR Christian Cabrera, Andrei Paleyes, Neil D. Lawrence
2023	—	`conf`	Automated Discovery of Trade-Off Between Utility, Privacy and Fairness in Machine Learning Models. PKDD/ECML Workshops (1) Bogdan Ficiu, Neil D. Lawrence, Andrei Paleyes
2023	J	`jnl`	Automated discovery of trade-off between utility, privacy and fairness in machine learning models. CoRR Bogdan Ficiu, Neil D. Lawrence, Andrei Paleyes
2023	—	`conf`	Causal fault localisation in dataflow systems. EuroMLSys@EuroSys Andrei Paleyes, Neil David Lawrence
2023	J	`jnl`	Causal fault localisation in dataflow systems. CoRR Andrei Paleyes, Neil D. Lawrence
2023	J	`jnl`	Challenges in Deploying Machine Learning: A Survey of Case Studies. ACM Comput. Surv. Andrei Paleyes, Raoul-Gabriel Urma, Neil D. Lawrence
2023	B	`conf`	Dataflow graphs as complete causal graphs. CAIN Andrei Paleyes, Siyuan Guo, Bernhard Schölkopf, Neil D. Lawrence
2023	J	`jnl`	Dataflow graphs as complete causal graphs. CoRR Andrei Paleyes, Siyuan Guo, Bernhard Schölkopf, Neil D. Lawrence
2023	—	`conf`	Emukit: A Python toolkit for decision making under uncertainty. SciPy Andrei Paleyes, Maren Mahsereci, Neil D. Lawrence
2023	J	`jnl`	Real-world Machine Learning Systems: A survey from a Data-Oriented Architecture Perspective. CoRR Christian Cabrera, Andrei Paleyes, Pierre Thodoroff, Neil D. Lawrence
2023	J	`jnl`	Trieste: Efficiently Exploring The Depths of Black-box Functions with TensorFlow. CoRR 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
2022	J	`jnl`	A penalisation method for batch multi-objective Bayesian optimisation with application in heat exchanger design. CoRR Andrei Paleyes, Henry B. Moss, Victor Picheny, Piotr Zulawski, Felix Newman
2022	J	`jnl`	An Empirical Evaluation of Flow Based Programming in the Machine Learning Deployment Context. CoRR Andrei Paleyes, Christian Cabrera, Neil D. Lawrence
2022	B	`conf`	An empirical evaluation of flow based programming in the machine learning deployment context. CAIN Andrei Paleyes, Christian Cabrera, Neil D. Lawrence
2022	J	`jnl`	Desiderata for next generation of ML model serving. CoRR Sherif Akoush, Andrei Paleyes, Arnaud Van Looveren, Clive Cox
2021	J	`jnl`	Emulation of physical processes with Emukit. CoRR Andrei Paleyes, Mark Pullin, Maren Mahsereci, Cliff McCollum, Neil D. Lawrence, Javier González
2021	J	`jnl`	Exploring the potential of flow-based programming for machine learning deployment in comparison with service-oriented architectures. CoRR Andrei Paleyes, Christian Cabrera, Neil D. Lawrence
2020	J	`jnl`	Automatic Discovery of Privacy-Utility Pareto Fronts. Proc. Priv. Enhancing Technol. Brendan Avent, Javier González, Tom Diethe, Andrei Paleyes, Borja Balle
2020	A	`conf`	Causal Bayesian Optimization. AISTATS Virginia Aglietti, Xiaoyu Lu, Andrei Paleyes, Javier González
2020	J	`jnl`	Causal Bayesian Optimization. CoRR Virginia Aglietti, Xiaoyu Lu, Andrei Paleyes, Javier González
2020	J	`jnl`	Challenges in Deploying Machine Learning: a Survey of Case Studies. CoRR Andrei Paleyes, Raoul-Gabriel Urma, Neil D. Lawrence
2020	J	`jnl`	Good practices for Bayesian Optimization of high dimensional structured spaces. CoRR Eero Siivola, Javier González, Andrei Paleyes, Aki Vehtari
2019	J	`jnl`	Automatic Discovery of Privacy-Utility Pareto Fronts. CoRR Brendan Avent, Javier González, Tom Diethe, Andrei Paleyes, Borja Balle