72 citations · 218 across the 27 of their papers we have counts for
10 papers · 1 filter
Bridging the reality gap in quantum devices with physics-aware machine learning
D. L. Craig, H. Moon, F. Fedele +9
The discrepancies between reality and simulation impede the optimisation and scalability of solid-state quantum devices. Disorder induced by the unpredictable distribution of mater…
A Kernel Test for Causal Association via Noise Contrastive Backdoor Adjustment
Robert Hu, Dino Sejdinovic, Robin J. Evans
Causal inference grows increasingly complex as the number of confounders increases. Given treatments , confounders and outcomes , we develop a non-parametric method to te…
RKHS-SHAP: Shapley Values for Kernel Methods
Siu Lun Chau, Robert Hu, Javier Gonzalez +1
Feature attribution for kernel methods is often heuristic and not individualised for each prediction. To address this, we turn to the concept of Shapley values~(SV), a coalition ga…
Cross-architecture Tuning of Silicon and SiGe-based Quantum Devices Using Machine Learning
B. Severin, D. T. Lennon, L. C. Camenzind +20
The potential of Si and SiGe-based devices for the scaling of quantum circuits is tainted by device variability. Each device needs to be tuned to operation conditions. We give a ke…
BayesIMP: Uncertainty Quantification for Causal Data Fusion
Siu Lun Chau, Jean-François Ton, Javier González +2
While causal models are becoming one of the mainstays of machine learning, the problem of uncertainty quantification in causal inference remains challenging. In this paper, we stud…
Unrepresentative Big Surveys Significantly Overestimate US Vaccine Uptake
Valerie C. Bradley, Shiro Kuriwaki, Michael Isakov +3
Surveys are a crucial tool for understanding public opinion and behavior, and their accuracy depends on maintaining statistical representativeness of their target populations by mi…