1 code implementation • 22 Feb 2022 • Kirtan Padh, Jakob Zeitler, David Watson, Matt Kusner, Ricardo Silva, Niki Kilbertus
Causal effect estimation is important for many tasks in the natural and social sciences.
no code implementations • 22 Jun 2021 • Julius von Kügelgen, Nikita Agarwal, Jakob Zeitler, Afsaneh Mastouri, Bernhard Schölkopf
Algorithmic recourse aims to provide actionable recommendations to individuals to obtain a more favourable outcome from an automated decision-making system.
no code implementations • 28 Feb 2022 • Jakob Zeitler, Ricardo Silva
Due to unmeasured confounding, it is often not possible to identify causal effects from a postulated model.
no code implementations • 18 Jan 2023 • Jakob Zeitler, Athanasios Vlontzos, Ciaran M. Gilligan-Lee
While identifiability of the causal estimand in such models has been obtained from a range of assumptions, it is widely and implicitly assumed that the underlying assumptions are satisfied for all time periods both pre- and post-intervention.
no code implementations • 6 Nov 2023 • Mahdi Eskandari, Lars Puiman, Jakob Zeitler
A Bayesian optimization approach for maximizing the gas conversion rate in an industrial-scale bioreactor for syngas fermentation is presented.
no code implementations • 6 Dec 2023 • Hao Wen, Jakob Zeitler, Connor Rupnow
To minimize station downtime and maximize experimental throughput, it is practical to run experiments in asynchronous parallel, in which multiple experiments are being performed at once in different stages.
no code implementations • 19 Dec 2023 • Gbetondji J-S Dovonon, Jakob Zeitler
Multi-fidelity Bayesian Optimisation (MFBO) has been shown to generally converge faster than single-fidelity Bayesian Optimisation (SFBO) (Poloczek et al. (2017)).