Towards Automated Knowledge Integration From Human-Interpretable Representations

TL;DR

This work tackles the challenge of automating inductive bias selection by learning a mapping from human knowledge representations to model priors. It introduces informed meta-learning and provides Informed Neural Processes (INP) as a concrete instantiation that conditions priors on knowledge ${\mathcal{K}}$ alongside context data ${\mathcal{D}}_C$. Through synthetic 1-D tasks and real-world data (weather and CUB), the approach yields improved data efficiency, better generalization under distribution shifts, and meaningful uncertainty reduction when knowledge is available. The authors also discuss limitations, including meta-training data requirements and the trade-offs relative to exact Bayesian knowledge integration, and highlight potential synergies with large language models for knowledge generation and augmentation.

Abstract

A significant challenge in machine learning, particularly in noisy and low-data environments, lies in effectively incorporating inductive biases to enhance data efficiency and robustness. Despite the success of informed machine learning methods, designing algorithms with explicit inductive biases remains largely a manual process. In this work, we explore how prior knowledge represented in its native formats, e.g. in natural language, can be integrated into machine learning models in an automated manner. Inspired by the learning to learn principles of meta-learning, we consider the approach of learning to integrate knowledge via conditional meta-learning, a paradigm we refer to as informed meta-learning. We introduce and motivate theoretically the principles of informed meta-learning enabling automated and controllable inductive bias selection. To illustrate our claims, we implement an instantiation of informed meta-learning--the Informed Neural Process, and empirically demonstrate the potential benefits and limitations of informed meta-learning in improving data efficiency and generalisation.

Figures (12)

• Figure 1: Knowledge representations ${\mathcal{K}}_i$ condition the heterogeneous learnable prior $p_\theta(f)$. The knowledge-conditioned priors $p_\theta(f \vert {\mathcal{K}}_i)$ are more tightly concentrated around the ground truth data-generating functions $f_i$, facilitating stronger inductive biases.
• Figure 2: The generating process of data and knowledge.
• Figure 3: Informed meta-learning. Successful knowledge integration via meta-learning ensures that predictions obtained with the informed marginal: $p_\theta(y \vert x, {\mathcal{D}}_C, {\mathcal{K}}_C)$: a) improve upon the uninformed predictions obtained with $p_\theta(y \vert x, {\mathcal{D}}_C)$; b) qualitatively reflect our knowledge of the DGP.
• Figure 4: .
• Figure 5: a) Average log-likelihood on training vs. testing tasks. b) Top: Log(loss) on testing tasks. Bottom: Frequency of tasks for a given value of $b$ observed during training. Results presented for zero-shot tasks with ${\mathcal{D}}_C = \varnothing$. Bars represent 1 standard deviation across the tasks within one bin of the $b$ parameter values. Providing knowledge about the parameter $b$ helps in generalisation to OOD tasks. INP generalises to previously unobserved values of $b$.

Theorems & Definitions (10)

• Example 1: MAML
• Example 2: Amortised meta-learners
• Theorem 1
• proof
• Theorem 1
• proof : Proof of Theorem 1 (informal)
• Proposition 1
• proof
• Corollary 1
• proof