6 citations · 8 across the 5 of their papers we have counts for
5 papers
Bayesian Optimization via Continual Variational Last Layer Training
Paul Brunzema, Mikkel Jordahn, John Willes +3
Gaussian Processes (GPs) are widely seen as the state-of-the-art surrogate models for Bayesian optimization (BO) due to their ability to model uncertainty and their performance on…
Variational Bayesian Last Layers
James Harrison, John Willes, Jasper Snoek
We introduce a deterministic variational formulation for training Bayesian last layer neural networks. This yields a sampling-free, single-pass model and loss that effectively impr…
A Comparison of Classical and Deep Reinforcement Learning Methods for HVAC Control
Marshall Wang, John Willes, Thomas Jiralerspong +1
Reinforcement learning (RL) is a promising approach for optimizing HVAC control. RL offers a framework for improving system performance, reducing energy consumption, and enhancing…
InterTrack: Interaction Transformer for 3D Multi-Object Tracking
John Willes, Cody Reading, Steven L. Waslander
3D multi-object tracking (MOT) is a key problem for autonomous vehicles, required to perform well-informed motion planning in dynamic environments. Particularly for densely occupie…
Bayesian Embeddings for Few-Shot Open World Recognition
John Willes, James Harrison, Ali Harakeh +3
As autonomous decision-making agents move from narrow operating environments to unstructured worlds, learning systems must move from a closed-world formulation to an open-world and…