Energy
Pointwise uncertainty quantification for sparse variational Gaussian process regression with a Brownian motion prior
We study pointwise estimation and uncertainty quantification for a sparse varia-tional Gaussian process method with eigenvector inducing variables.
Single Model Uncertainty Estimation via Stochastic Data Centering
UQ's uncertainty estimates are superior to many of the current methods
A Provably Efficient Sample Collection Strategy for Reinforcement Learning
One of the challenges in online reinforcement learning (RL) is that the agent needs to trade off the exploration of the environment and the exploitation of the samples to optimize its behavior. Whether we optimize for regret, sample complexity, state-space coverage or model estimation, we need to strike a different exploration-exploitation trade-off.
A Provably Efficient Sample Collection Strategy for Reinforcement Learning
One of the challenges in online reinforcement learning (RL) is that the agent needs to trade off the exploration of the environment and the exploitation of the samples to optimize its behavior. Whether we optimize for regret, sample complexity, state-space coverage or model estimation, we need to strike a different exploration-exploitation trade-off.
A Proof of Theorem 2
We prove the universal approximation theorem by showing the equivalence of TFN and our model. Complex spherical harmonics are related to Clebsch-Gordan coefficients via [51, 3.7.72] We can therefore adapt Eq. (2) by substituting C To see this, we look at the result's real component null [ H To prove this theorem we first introduce a proposition by Villar et al. [57]. GemNet's variance varies strongly between layers and increases significantly after each block without scaling factors (top). We use 4 stacked interaction blocks and an embedding size of 128 throughout the model.