Publications (5)
Model-Based Bayesian Exploration
Richard Dearden, Nir Friedman, David Andre
Reinforcement learning systems are often concerned with balancing exploration of untested actions against exploitation of actions that are known to be good. The benefit of explorat…
A compact, hierarchical Q-function decomposition
Bhaskara Marthi, Stuart Russell, David Andre
Previous work in hierarchical reinforcement learning has faced a dilemma: either ignore the values of different possible exit states from a subroutine, thereby risking suboptimal b…
Earth AI: Unlocking Geospatial Insights with Foundation Models and Cross-Modal Reasoning
Aaron Bell, Amit Aides, Amr Helmy +57
Geospatial data offers immense potential for understanding our planet. However, the sheer volume and diversity of this data along with its varied resolutions, timescales, and spars…
Scalable Geospatial Data Generation Using AlphaEarth Foundations Model
Luc Houriez, Sebastian Pilarski, Behzad Vahedi +11
High-quality labeled geospatial datasets are essential for extracting insights and understanding our planet. Unfortunately, these datasets often do not span the entire globe and ar…
LMD3: Language Model Data Density Dependence
John Kirchenbauer, Garrett Honke, Gowthami Somepalli +5
We develop a methodology for analyzing language model task performance at the individual example level based on training data density estimation. Experiments with paraphrasing as a…