Accelerating the discovery of novel and more effective therapeutics is a...
Bayesian optimization is a highly efficient approach to optimizing objec...
A popular approach to protein design is to combine a generative model wi...
In this paper, we provide an information-theoretic perspective on
Varian...
Sequential Bayesian inference can be used for continual learning to prev...
KL-regularized reinforcement learning from expert demonstrations has pro...
Bayesian deep learning seeks to equip deep neural networks with the abil...
Offline reinforcement learning has shown great promise in leveraging lar...
High-quality estimates of uncertainty and robustness are crucial for num...
While reinforcement learning algorithms provide automated acquisition of...
We show that the gradient estimates used in training Deep Gaussian Proce...
Inter-domain Gaussian processes (GPs) allow for high flexibility and low...
Evaluation of Bayesian deep learning (BDL) methods is challenging. We of...
In the last few years, deep multi-agent reinforcement learning (RL) has
...
We propose a novel approach for rapid segmentation of flooded buildings ...
Applying probabilistic models to reinforcement learning (RL) has become ...