Causal Bayesian Optimization. - Virginia Aglietti; Xiaoyu Lu; Andrei Paleyes; Javier González 002 (AISTATS, 2020)

Good practices for Bayesian Optimization of high dimensional structured spaces. - Eero Siivola; Javier González 002; Andrei Paleyes; Aki Vehtari (CoRR, 2020)

Automatic Discovery of Privacy-Utility Pareto Fronts. - Brendan Avent; Javier González 002; Tom Diethe; Andrei Paleyes; Borja Balle (Proc. Priv. Enhancing Technol., 2020)

Exploring the potential of flow-based programming for machine learning deployment in comparison with service-oriented architectures. - Andrei Paleyes; Christian Cabrera 001; Neil D. Lawrence (CoRR, 2021)

Emulation of physical processes with Emukit. - Andrei Paleyes; Mark Pullin; Maren Mahsereci; Cliff McCollum; Neil D. Lawrence; Javier González 002 (CoRR, 2021)

An empirical evaluation of flow based programming in the machine learning deployment context. - Andrei Paleyes; Christian Cabrera 001; Neil D. Lawrence (CAIN, 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)

Desiderata for next generation of ML model serving. - Sherif Akoush; Andrei Paleyes; Arnaud Van Looveren; Clive Cox (CoRR, 2022)

Dataflow graphs as complete causal graphs. - Andrei Paleyes; Siyuan Guo; Bernhard Schölkopf; Neil D. Lawrence (CAIN, 2023)

Causal fault localisation in dataflow systems. - Andrei Paleyes; Neil David Lawrence (EuroMLSys@EuroSys, 2023)

Automated Discovery of Trade-Off Between Utility, Privacy and Fairness in Machine Learning Models. - Bogdan Ficiu; Neil D. Lawrence; Andrei Paleyes (PKDD/ECML Workshops (1), 2023)

Real-world Machine Learning Systems: A survey from a Data-Oriented Architecture Perspective. - Christian Cabrera 001; Andrei Paleyes; Pierre Thodoroff; Neil D. Lawrence (CoRR, 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; et al. (CoRR, 2023)

Challenges in Deploying Machine Learning: A Survey of Case Studies. - Andrei Paleyes; Raoul-Gabriel Urma; Neil D. Lawrence (ACM Comput. Surv., 2023)

Can causality accelerate experimentation in software systems? - Andrei Paleyes; Han-Bo Li; Neil D. Lawrence (CAIN, 2024)

Self-sustaining Software Systems (S4): Towards Improved Interpretability and Adaptation. - Christian Cabrera 001; Andrei Paleyes; Neil D. Lawrence (SATrends, 2024)

LLM Performance for Code Generation on Noisy Tasks. - Radzim Sendyka; Christian Cabrera 001; Andrei Paleyes; Diana Robinson; Neil D. Lawrence (CoRR, 2025)

Prompt Variability Effects On LLM Code Generation. - Andrei Paleyes; Radzim Sendyka; Diana Robinson; Christian Cabrera 001; Neil D. Lawrence (CoRR, 2025)