Search Results for author:
Found 3 papers, 1 papers with code
Pretty darn good control: when are approximate solutions better than approximate models
But when is the optimal solution to an approximate, stylized model better than an approximate solution to a more accurate model?
Reflections from the Workshop on AI-Assisted Decision Making for Conservation
In this white paper, we synthesize key points made during presentations and discussions from the AI-Assisted Decision Making for Conservation workshop, hosted by the Center for Research on Computation and Society at Harvard University on October 20-21, 2022.
Power and accountability in reinforcement learning applications to environmental policy
Machine learning (ML) methods already permeate environmental decision-making, from processing high-dimensional data on earth systems to monitoring compliance with environmental regulations.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
