Search Results for author: Jakob Zeitler

Found 7 papers, 1 papers with code

Long-run Behaviour of Multi-fidelity Bayesian Optimisation

no code implementations19 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)).

Bayesian Optimisation

Search Strategies for Self-driving Laboratories with Pending Experiments

no code implementations6 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.

Bayesian Optimisation

Multi-fidelity Bayesian Optimisation of Syngas Fermentation Simulators

no code implementations6 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.

Non-parametric identifiability and sensitivity analysis of synthetic control models

no code implementations18 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.

Causal Inference

The Causal Marginal Polytope for Bounding Treatment Effects

no code implementations28 Feb 2022 Jakob Zeitler, Ricardo Silva

Due to unmeasured confounding, it is often not possible to identify causal effects from a postulated model.

Stochastic Causal Programming for Bounding Treatment Effects

1 code implementation22 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.

Algorithmic Recourse in Partially and Fully Confounded Settings Through Bounding Counterfactual Effects

no code implementations22 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.

counterfactual Decision Making

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