Publications (33)
Unexpected Improvements to Expected Improvement for Bayesian Optimization
Sebastian Ament, Samuel Daulton, David Eriksson +2
Expected Improvement (EI) is arguably the most popular acquisition function in Bayesian optimization and has found countless successful applications, but its performance is often e…
2HDMC - Two-Higgs-Doublet Model Calculator
David Eriksson, Johan Rathsman, Oscar Stål
This manual describes the public code 2HDMC which can be used to perform calculations in a general, CP-conserving, two-Higgs-doublet model (2HDM). The program features simple conve…
Associated charged Higgs and W boson production in the MSSM at the CERN Large Hadron Collider
David Eriksson, Stefan Hesselbach, Johan Rathsman
We investigate the viability of observing charged Higgs bosons (H^+/-) produced in association with W bosons at the CERN Large Hadron Collider, using the leptonic decay H^+ -> tau^…
Scalable Log Determinants for Gaussian Process Kernel Learning
Kun Dong, David Eriksson, Hannes Nickisch +2
For applications as varied as Bayesian neural networks, determinantal point processes, elliptical graphical models, and kernel learning for Gaussian processes (GPs), one must compu…
Sample-Efficient Bayesian Optimization with Transfer Learning for Heterogeneous Search Spaces
Aryan Deshwal, Sait Cakmak, Yuhou Xia +1
Bayesian optimization (BO) is a powerful approach to sample-efficient optimization of black-box functions. However, in settings with very few function evaluations, a successful app…
Informed Initialization for Bayesian Optimization and Active Learning
Carl Hvarfner, David Eriksson, Eytan Bakshy +1
Bayesian Optimization is a widely used method for optimizing expensive black-box functions, relying on probabilistic surrogate models such as Gaussian Processes. The quality of the…