133 citations · 322 across the 19 of their papers we have counts for
4 papers · 1 filter
Neural Spatio-Temporal Point Processes
Ricky T. Q. Chen, Brandon Amos, Maximilian Nickel
We propose a new class of parameterizations for spatio-temporal point processes which leverage Neural ODEs as a computational method and enable flexible, high-fidelity models of di…
On the model-based stochastic value gradient for continuous reinforcement learning
Brandon Amos, Samuel Stanton, Denis Yarats +1
For over a decade, model-based reinforcement learning has been seen as a way to leverage control-based domain knowledge to improve the sample-efficiency of reinforcement learning a…
Aligning Time Series on Incomparable Spaces
Samuel Cohen, Giulia Luise, Alexander Terenin +2
Dynamic time warping (DTW) is a useful method for aligning, comparing and combining time series, but it requires them to live in comparable spaces. In this work, we consider a sett…
Objective Mismatch in Model-based Reinforcement Learning
Nathan Lambert, Brandon Amos, Omry Yadan +1
Model-based reinforcement learning (MBRL) has been shown to be a powerful framework for data-efficiently learning control of continuous tasks. Recent work in MBRL has mostly focuse…