14 citations · 27 across the 3 of their papers we have counts for
3 papers
cs.LG2024★ 1 cited
Deep Gaussian Covariance Network with Trajectory Sampling for Data-Efficient Policy Search
Can Bogoclu, Robert Vosshall, Kevin Cremanns +1
Probabilistic world models increase data efficiency of model-based reinforcement learning (MBRL) by guiding the policy with their epistemic uncertainty to improve exploration and a…
stat.ML2023★ 12 cited
Gradient and Uncertainty Enhanced Sequential Sampling for Global Fit
Sven Lämmle, Can Bogoclu, Kevin Cremanns +1
Surrogate models based on machine learning methods have become an important part of modern engineering to replace costly computer simulations. The data used for creating a surrogat…
cs.LG2017★ 14 cited
Deep Gaussian Covariance Network
Kevin Cremanns, Dirk Roos
The correlation length-scale next to the noise variance are the most used hyperparameters for the Gaussian processes. Typically, stationary covariance functions are used, which are…