# A Scalable Technique for Weak-Supervised Learning with Domain Constraints Source: [A Scalable Technique for Weak-Supervised Learning with Domain Constraints]() Collection index: [llms.txt]() Data updated: 2025-10-26T01:52:52.049Z Authors: Sudhir Agarwal, Anu Sreepathy, Lalla Mouatadid Published: 2023-01-12T19:04:42.000Z ## Abstract We propose a novel scalable end-to-end pipeline that uses symbolic domain knowledge as constraints for learning a neural network for classifying unlabeled data in a weak-supervised manner. Our approach is particularly well-suited for settings where the data consists of distinct groups (classes) that lends itself to clustering-friendly representation learning and the domain constraints can be reformulated for use of efficient mathematical optimization techniques by considering multiple training examples at once. We evaluate our approach on a variant of the MNIST image classification problem where a training example consists of image sequences and the sum of the numbers represented by the sequences, and show that our approach scales significantly better than previous approaches that rely on computing all constraint satisfying combinations for each training example. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()