319 citations · 1.2k across the 28 of their papers we have counts for
4 papers · 1 filter
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…
Transfer Learning with Gaussian Processes for Bayesian Optimization
Petru Tighineanu, Kathrin Skubch, Paul Baireuther +3
Bayesian optimization is a powerful paradigm to optimize black-box functions based on scarce and noisy data. Its data efficiency can be further improved by transfer learning from r…
No-Regret Bayesian Optimization with Unknown Hyperparameters
Felix Berkenkamp, Angela P. Schoellig, Andreas Krause
Bayesian optimization (BO) based on Gaussian process models is a powerful paradigm to optimize black-box functions that are expensive to evaluate. While several BO algorithms prova…
Safe Model-based Reinforcement Learning with Stability Guarantees
Felix Berkenkamp, Matteo Turchetta, Angela P. Schoellig +1
Reinforcement learning is a powerful paradigm for learning optimal policies from experimental data. However, to find optimal policies, most reinforcement learning algorithms explor…