Join our regular research seminars, message Jakob for access (firstname.lastname@example.org)
The authors propose HPOBench, which includes 7 existing and 5 new benchmark families, with a total of more than 100 multi-fidelity benchmark problems.
Aug. 14, 2023
The authors resolve the important open problem of deriving regret bounds for this setting, which imply novel convergence rates for GP optimization.
July 25, 2023
The authors propose rMFBO (robust MFBO), a methodology to make any GP-based MFBO scheme robust to the addition of unreliable information sources. rMFBO comes with a theoretical guarantee that its performance can be bound to its vanilla BO analog.
July 12, 2023
In this paper, the authors study BNNs as alternatives to standard GP surrogates for optimization. We consider a variety of approximate inference procedures for finite-width BNNs, including
July 7, 2023
In this work, the authors provide an extensive study of the relationship between the BO performance (regret) and uncertainty calibration for popular surrogate models and compare them across both synthetic and real-world experiments.
June 22, 2023
This paper studies the problem of globally optimizing a variable of interest that is part of a causal model in which a sequence of interventions can be performed. This problem arises in biology, operational research, communications.
March 7, 2023
Get in touch
We will happily guide you through the emerging space of data-driven material discovery. We look forward to learn from your experience and problem space.