Seminars
Join our regular research seminars, message Jakob for access (jakob@matterhorn.studio)
(November Series #4) Long-run Behaviour of Multi-fidelity Bayesian Optimisation
Join our November Research Series Talk #4 on November 28th at 2pm!
Nov. 24, 2023
(November Series #3) Closed-loop Optimisation of Deformable Mirrors for Laser Beam Aberration Correction
Join our November Research Series Talk #3 next Tuesday 5th Dec at 2pm!
Nov. 17, 2023
(November Series #2) Syngas Fermentation Optimisation with Mahdi Eskandari
Join our November Research Series Talk #2 next Tuesday at 2pm London!
Nov. 10, 2023
(November Series #1) Search strategies for asynchronous parallel self-driving laboratories with pending points
Join our November Research Series Talk #1 next Wednesday at 2pm!
Nov. 1, 2023
HPOBench: A Collection of Reproducible Multi-Fidelity Benchmark Problems for HPO
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
Gaussian Process Optimization in the Bandit Setting: No Regret and Experimental Design
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
Multi-Fidelity Bayesian Optimization with Unreliable Information Sources
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
A Study of Bayesian Neural Network Surrogates for Bayesian Optimization
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
On the role of Model Uncertainties in Bayesian Optimization
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
Causal Bayesian Optimization
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
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