Inferring Generative Model Structure with Static Analysis (1709.02477v1)

Abstract: Obtaining enough labeled data to robustly train complex discriminative models is a major bottleneck in the machine learning pipeline. A popular solution is combining multiple sources of weak supervision using generative models. The structure of these models affects training label quality, but is difficult to learn without any ground truth labels. We instead rely on these weak supervision sources having some structure by virtue of being encoded programmatically. We present Coral, a paradigm that infers generative model structure by statically analyzing the code for these heuristics, thus reducing the data required to learn structure significantly. We prove that Coral's sample complexity scales quasilinearly with the number of heuristics and number of relations found, improving over the standard sample complexity, which is exponential in $n$ for identifying $n^{\textrm{th}}$ degree relations. Experimentally, Coral matches or outperforms traditional structure learning approaches by up to 3.81 F1 points. Using Coral to model dependencies instead of assuming independence results in better performance than a fully supervised model by 3.07 accuracy points when heuristics are used to label radiology data without ground truth labels.

• The paper introduces the Coral paradigm, which leverages static analysis to infer generative model structure without extensive labeled data.
• It constructs factor graphs from programmatically encoded heuristics to efficiently model dependencies among weak supervision sources.
• Empirical results show up to a 3.81-point F1 score improvement in diverse domains such as medical imaging and bone tumor classification.

Inferring Generative Model Structure with Static Analysis

The paper "Inferring Generative Model Structure with Static Analysis" presents the Coral paradigm, a novel approach that leverages static code analysis to infer the structure of generative models used in aggregating weak supervision sources. Traditional approaches to learning generative model structures typically rely on supervised data or user-specified dependencies, but Coral reduces the prerequisite data significantly by systematically inspecting programmatically encoded heuristics. This paper makes substantial claims regarding the efficiency and effectiveness of Coral in modeling dependencies among heuristics and primitives, leading to improved prediction performance.

Problem Statement and Motivation

Complex discriminative models, such as deep neural networks, require vast amounts of labeled data for robust training. However, obtaining sufficient labeled data is often a significant bottleneck in the machine learning pipeline. Weak supervision—through sources such as heuristics and knowledge bases—addresses this issue by generating training labels for unlabeled data. Generative models have emerged as potent tools for leveraging weak supervision to infer true class labels by modeling them as latent variables. A pivotal challenge in this context is the accurate specification of the model structure to optimize the effectiveness of supervision sources, especially in scenarios lacking ground truth labels. Coral aims to solve this problem by utilizing static analysis of code, effectively reducing sample complexity to quasilinear, contrary to the exponential growth commonly associated with higher degree dependency models.

Methodology

Coral provides a framework where domain-specific primitives and heuristic functions are programmatically specified as inputs. The static analysis performed by Coral exploits the structure of these heuristic functions to infer dependencies without requiring data. This method identifies relations based on heuristics that share input primitives. By constructing a factor graph, Coral represents relationships between the heuristics, primitives, and latent class labels, enabling more accurate dependency modeling.

Results and Validation

Through empirical validation across diverse domains such as bone tumor classification and image querying, Coral demonstrates marked improvement over traditional independent and structure learning approaches. In scenarios involving complex heuristic dependencies, Coral consistently outperforms baseline methods, showing up to 3.81 points improvement in F1 score. Further, Coral matches or exceeds the performance of fully supervised models even when used to label radiology data without ground truth, underscoring its practical utility.

Implications and Future Directions

The findings from Coral suggest significant implications for the practical deployment of machine learning systems where labeled datasets are scarce or costly to obtain. By leveraging programmatic insights, Coral holds promise for extending weak supervision methodologies to a broader range of applications, including those in computer vision and medical imaging. Theoretical advancements could explore integrating more sophisticated static analysis techniques or combining programmatic encoding with empirically validated methods to enhance generative model accuracy.

In conclusion, Coral embodies a strategic shift towards reducing dependency on labeled data through thoughtful utilization of programmatically structured heuristics. This work contributes to the ongoing optimization of generative models in handling weak supervision and opens new avenues for efficiently harnessing unlabeled data in machine learning workflows. Future explorations aimed at refining and contextualizing the Coral methodology could further integrate insights from programming languages into the domain of machine learning, fostering symbiotic advancements in both fields.