Convex and Bilevel Optimization for Neuro-Symbolic Inference and Learning
CoRR(2024)
Abstract
We address a key challenge for neuro-symbolic (NeSy) systems by leveraging
convex and bilevel optimization techniques to develop a general gradient-based
framework for end-to-end neural and symbolic parameter learning. The
applicability of our framework is demonstrated with NeuPSL, a state-of-the-art
NeSy architecture. To achieve this, we propose a smooth primal and dual
formulation of NeuPSL inference and show learning gradients are functions of
the optimal dual variables. Additionally, we develop a dual block coordinate
descent algorithm for the new formulation that naturally exploits warm-starts.
This leads to over 100x learning runtime improvements over the current best
NeuPSL inference method. Finally, we provide extensive empirical evaluations
across 8 datasets covering a range of tasks and demonstrate our learning
framework achieves up to a 16
alternative learning methods.
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