Learning from Uncertain Data: From Possible Worlds to Possible Models
CoRR(2024)
Abstract
We introduce an efficient method for learning linear models from uncertain
data, where uncertainty is represented as a set of possible variations in the
data, leading to predictive multiplicity. Our approach leverages abstract
interpretation and zonotopes, a type of convex polytope, to compactly represent
these dataset variations, enabling the symbolic execution of gradient descent
on all possible worlds simultaneously. We develop techniques to ensure that
this process converges to a fixed point and derive closed-form solutions for
this fixed point. Our method provides sound over-approximations of all possible
optimal models and viable prediction ranges. We demonstrate the effectiveness
of our approach through theoretical and empirical analysis, highlighting its
potential to reason about model and prediction uncertainty due to data quality
issues in training data.
MoreTranslated text
AI Read Science
Must-Reading Tree
Example
![](https://originalfileserver.aminer.cn/sys/aminer/pubs/mrt_preview.jpeg)
Generate MRT to find the research sequence of this paper
Chat Paper
Summary is being generated by the instructions you defined