4 papers
SMOG: Scalable Meta-Learning for Multi-Objective Bayesian Optimization
Leonard Papenmeier, Petru Tighineanu
Multi-objective optimization aims to solve problems with competing objectives. Evaluating such problems is often slow or expensive, limiting the budget of evaluations. In many appl…
Scalable Meta-Learning with Gaussian Processes
Petru Tighineanu, Lukas Grossberger, Paul Baireuther +4
Meta-learning is a powerful approach that exploits historical data to quickly solve new tasks from the same distribution. In the low-data regime, methods based on the closed-form p…
Suppressing phonon decoherence of high performance single-photon sources in nanophotonic waveguides
Chris L. Dreeßen, Claudéric Oullet-Plamondon, Petru Tighineanu +4
The fundamental process limiting the coherence of quantum-dot based single-photon sources is the interaction with phonons. We study the effect of phonon decoherence on the indistin…
Reinforcement Learning with Neural Networks for Quantum Feedback
Thomas Fösel, Petru Tighineanu, Talitha Weiss +1
Machine learning with artificial neural networks is revolutionizing science. The most advanced challenges require discovering answers autonomously. This is the domain of reinforcem…