# Chameleon: Learning Model Initializations Across Tasks With Different Schemas

arXiv: https://arxiv.org/abs/1909.13576 · HTML: https://mlsanity.com/paper/1909.13576/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Sep 2019.

## Built on by (most cited first, 4 of 4)

- [Efficiently Identifying Task Groupings for Multi-Task Learning](https://mlsanity.com/paper/2109.04617/) (2021)
- [Few-Shot Forecasting of Time-Series with Heterogeneous Channels](https://mlsanity.com/paper/2204.03456/) (2022)
- [Meta-learning of semi-supervised learning from tasks with heterogeneous attribute spaces](https://mlsanity.com/paper/2311.05088/) (2023)
- [Few-shot human motion prediction for heterogeneous sensors](https://mlsanity.com/paper/2212.11771/) (2022)

## Abstract (arXiv)

Parametric models, and particularly neural networks, require weight initialization as a starting point for gradient-based optimization. Recent work shows that a specific initial parameter set can be learned from a population of supervised learning tasks. Using this initial parameter set enables a fast convergence for unseen classes even when only a handful of instances is available (model-agnostic meta-learning). Currently, methods for learning model initializations are limited to a population of tasks sharing the same schema, i.e., the same number, order, type, and semantics of predictor and target variables. In this paper, we address the problem of meta-learning parameter initialization across tasks with different schemas, i.e., if the number of predictors varies across tasks, while they still share some variables. We propose Chameleon, a model that learns to align different predictor schemas to a common representation. In experiments on 23 datasets of the OpenML-CC18 benchmark, we show that Chameleon can successfully learn parameter initializations across tasks with different schemas, presenting, to the best of our knowledge, the first cross-dataset few-shot classification approach for unstructured data.