Energy
Data-Driven Conditional Robust Optimization
In most real world decision problems, the decision maker (DM) faces uncertainty either in the objective function that he aims to optimize, or some of the constraints that he needs to satisfy.
Checklist 1. For all authors (a)
In this section, we elaborate on the various details of our experiments.
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.
