Scaling Experiments in Self-Supervised Cross-Table Representation Learning (2309.17339v1)

Abstract: To analyze the scaling potential of deep tabular representation learning models, we introduce a novel Transformer-based architecture specifically tailored to tabular data and cross-table representation learning by utilizing table-specific tokenizers and a shared Transformer backbone. Our training approach encompasses both single-table and cross-table models, trained via missing value imputation through a self-supervised masked cell recovery objective. To understand the scaling behavior of our method, we train models of varying sizes, ranging from approximately $10^4$ to $10^7$ parameters. These models are trained on a carefully curated pretraining dataset, consisting of 135M training tokens sourced from 76 diverse datasets. We assess the scaling of our architecture in both single-table and cross-table pretraining setups by evaluating the pretrained models using linear probing on a curated set of benchmark datasets and comparing the results with conventional baselines.

• The paper investigates scaling experiments of Transformer-based architectures for cross-table learning using a self-supervised masked cell recovery task.
• It introduces a novel design combining table-specific tokenizers with a shared backbone, effectively addressing missing value imputation in diverse datasets.
• Experiments demonstrate that scaling improves performance on larger datasets, but benefits plateau on smaller ones, highlighting opportunities for further innovation.

Scaling Experiments in Self-Supervised Cross-Table Representation Learning

Self-supervised learning continues to capture significant attention due to its ability to learn representations without relying on labeled data. The paper "Scaling Experiments in Self-Supervised Cross-Table Representation Learning" tackles the persistent challenge of leveraging these techniques for tabular data, which often doesn't benefit from the same consistency in performance gains seen in other domains, such as natural language processing or computer vision.

Objective and Methodology

The primary objective of this work is to explore the scaling behavior of Transformer-based architectures applied to tabular data, particularly focusing on cross-table representation learning. The authors introduce a novel architecture employing table-specific tokenizers and a shared Transformer backbone, trained using a masked cell recovery objective. This self-supervised approach aims to recover missing values, a task intrinsic to tabular datasets.

Training these models across various configurations ranging from \num{1e4} to \num{1e7} parameters, the research aims to determine how scaling affects their performance. A well-curated pretraining dataset consisting of 135 million tokens from 76 diverse datasets informs this investigation. Models are evaluated via linear probing on benchmark datasets to compare their performance against traditional methods.

Key Insights and Contributions

1. Cross-Table Representation Learning: The authors emphasize the importance of cross-table generalization. Given that tabular datasets often vary greatly in terms of column characteristics and missing values, models that can generalize across disparate tables are more practical and potentially more powerful.
2. Transformer-Based Architecture: A clean and straightforward Transformer architecture is proposed as an alternative to the more complex architectures seen in other studies. This approach highlights the need to understand fundamental components like table tokenization before venturing into more convoluted designs.
3. Self-Supervised Pretraining: By training these models using masked cell recovery, the authors provide a framework where models can learn to impute missing values intrinsically. This imputation task naturally aligns with the structure of tabular data, drawing a parallel to the masked LLMing used in NLP.
4. Scalability and Performance Trade-offs: The paper highlights that while parameter scaling improves the performance on larger datasets, it reaches a saturation point on smaller datasets. This insight aligns with existing knowledge that scaling both model size and dataset size is crucial for performance gains.
5. Comparative Analysis: Contrasting their model's scaling behavior against traditional baselines like XGBoost, the paper shows that while significant, improvements do not guarantee surpassing strong, well-optimized baselines. Therefore, this indicates room for more robust architectural and methodological innovations.

Implications and Future Directions

The investigation into tabular representation learning models implies several future work paths. Optimization of self-supervised tasks specifically for tabular data seems crucial. Exploring more efficient tokenization schemes that better capture tabular data's intrinsic characteristics could enhance the training efficiency and effectiveness.

Moreover, the paper indirectly underscores the potential utility of pretrained tabular backbones in few-shot or zero-shot learning contexts, as well as their use as feature extractors. Such applications are particularly compelling in domains with limited annotated data or where rapid adaptation to new data is beneficial.

In conclusion, this work represents a thorough exploration of scaling self-supervised learning architectures for tabular data, with promising insights into how traditional deep learning successes can be investigated and adapted in the tabular domain. As such, it contributes to the broader goal of developing more versatile and powerful AI systems that can efficiently handle the diversity inherent in real-world data.