381 research outputs found
In-Context Learning for Few-Shot Molecular Property Prediction
In-context learning has become an important approach for few-shot learning in
Large Language Models because of its ability to rapidly adapt to new tasks
without fine-tuning model parameters. However, it is restricted to applications
in natural language and inapplicable to other domains. In this paper, we adapt
the concepts underpinning in-context learning to develop a new algorithm for
few-shot molecular property prediction. Our approach learns to predict
molecular properties from a context of (molecule, property measurement) pairs
and rapidly adapts to new properties without fine-tuning. On the FS-Mol and
BACE molecular property prediction benchmarks, we find this method surpasses
the performance of recent meta-learning algorithms at small support sizes and
is competitive with the best methods at large support sizes
MAPTree: Beating "Optimal" Decision Trees with Bayesian Decision Trees
Decision trees remain one of the most popular machine learning models today,
largely due to their out-of-the-box performance and interpretability. In this
work, we present a Bayesian approach to decision tree induction via maximum a
posteriori inference of a posterior distribution over trees. We first
demonstrate a connection between maximum a posteriori inference of decision
trees and AND/OR search. Using this connection, we propose an AND/OR search
algorithm, dubbed MAPTree, which is able to recover the maximum a posteriori
tree. Lastly, we demonstrate the empirical performance of the maximum a
posteriori tree both on synthetic data and in real world settings. On 16 real
world datasets, MAPTree either outperforms baselines or demonstrates comparable
performance but with much smaller trees. On a synthetic dataset, MAPTree also
demonstrates greater robustness to noise and better generalization than
existing approaches. Finally, MAPTree recovers the maxiumum a posteriori tree
faster than existing sampling approaches and, in contrast with those
algorithms, is able to provide a certificate of optimality. The code for our
experiments is available at https://github.com/ThrunGroup/maptree.Comment: 19 page
- …