ORCID Profile
0000-0003-4483-5643
Current Organisations
Trinity College Dublin
,
Ludwig-Maximilians-Universität München
,
University of Oxford
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Publisher: American Physical Society (APS)
Date: 18-06-2019
Publisher: IOP Publishing
Date: 22-07-2015
Publisher: American Physical Society (APS)
Date: 27-03-2023
Publisher: American Physical Society (APS)
Date: 03-12-2018
Publisher: IOP Publishing
Date: 17-09-2021
Abstract: In the setting of regression, the standard formulation of gradient boosting generates a sequence of improvements to a constant model. In this paper, we reformulate gradient boosting such that it is able to generate a sequence of improvements to a nonconstant model, which may contain prior knowledge or physical insight about the data generating process. Moreover, we introduce a simple variant of multi-target stacking that extends our approach to the setting of multi-target regression. An experiment on a real-world superconducting quantum device calibration dataset demonstrates that our approach outperforms the state-of-the-art calibration model even though it only receives a paucity of training ex les. Further, it significantly outperforms a well-known gradient boosting algorithm, known as LightGBM, as well as an entirely data-driven reimplementation of the calibration model, which suggests the viability of our approach.
Publisher: American Physical Society (APS)
Date: 11-03-2015
Publisher: American Physical Society (APS)
Date: 12-04-2017
Publisher: American Physical Society (APS)
Date: 27-12-2018
Publisher: IOP Publishing
Date: 05-2023
Abstract: Many real-world tasks include some kind of parameter estimation, i.e. the determination of a parameter encoded in a probability distribution. Often, such probability distributions arise from stochastic processes. For a stationary stochastic process with temporal correlations, the random variables that constitute it are identically distributed but not independent. This is the case, for instance, for quantum continuous measurements. In this article, we derive the asymptotic Fisher information rate for a stationary process with finite Markov order. We give a precise expression for this rate which is determined by the process’ conditional distribution up to its Markov order. Second, we demonstrate with suitable ex les that correlations may both enhance or h er the metrological precision. Indeed, unlike for entropic information quantities, in general nothing can be said about the sub- or super-additivity of the joint Fisher information in the presence of correlations. To illustrate our results, we apply them to thermometry on an Ising spin chain, considering nearest-neighbour and next-to-nearest neighbour coupling. In this case, the asymptotic Fisher information rate is directly connected to the specific heat capacity of the spin chain. We observe that the presence of correlations strongly enhances the estimation precision in an anti-ferromagnetic chain, while in a ferromagnetic chain this is not the case.
Publisher: American Physical Society (APS)
Date: 09-02-2018
Publisher: American Physical Society (APS)
Date: 23-06-2020
Publisher: American Physical Society (APS)
Date: 22-12-2020
Publisher: American Physical Society (APS)
Date: 11-06-2018
Location: United Kingdom of Great Britain and Northern Ireland
No related grants have been discovered for Felix Binder.