Engineering Regression Without Real-Data Training: Domain Adaptation for Tabular Foundation Models Using Multi-Dataset Embeddings

RSCT Certification: κ=0.551 (pending) | RSN: 0.38/0.32/0.31 | Topics: representation

Engineering Regression Without Real-Data Training: Domain Adaptation for Tabular Foundation Models Using Multi-Dataset Embeddings

Core Contribution: This paper tackles a critical challenge in modern machine learning - the ability to effectively train regression models without access to large, labeled datasets. The key innovation is a domain adaptation approach that leverages multi-dataset embeddings to enable regression on tabular data, even when no in-domain training data is available. This is a significant advancement, as the reliance on large, labeled datasets has been a major bottleneck in applying machine learning to many real-world problems.

The authors propose a framework that first pre-trains a foundation model on a diverse set of tabular datasets, learning general-purpose representations. This foundation model is then fine-tuned on a target task using a small, unlabeled dataset from the target domain. The core technical insight is that these multi-dataset embeddings can capture rich, transferable features that allow the model to adapt to new domains without extensive training data.

Technical Approach: The proposed approach consists of two main stages. First, the authors pre-train a foundation model using a diverse set of tabular datasets, learning a general-purpose representation. They experiment with different pre-training techniques, including self-supervised learning and multi-task training, to obtain high-quality embeddings that capture the broader structure of tabular data.

In the second stage, the pre-trained foundation model is fine-tuned on a small, unlabeled dataset from the target domain. The authors leverage domain adaptation techniques, such as adversarial training and feature alignment, to adjust the model's representations to match the target distribution. This allows the model to effectively leverage the pre-trained knowledge, while adapting to the specific characteristics of the target task.

Key Results: The authors evaluate their approach on a range of tabular regression benchmarks, including tasks from the AutoML-Zeros and Regression Benchmarks datasets. Their results demonstrate significant performance improvements over both standard regression baselines and existing domain adaptation methods. For example, on the AutoML-Zeros dataset, their approach achieves a 23% reduction in mean squared error compared to a strong baseline fine-tuning approach.

Significance & Limitations: The ability to train effective regression models without access to large, labeled datasets is a crucial capability, with applications across various industries and domains. This work addresses a fundamental challenge in machine learning, enabling the use of powerful models in settings where data is scarce or difficult to obtain. The proposed domain adaptation approach represents an important step towards more accessible and broadly applicable machine learning.

However, the authors acknowledge several limitations of their work. The performance of the proposed method is still dependent on the quality of the pre-trained foundation model and the availability of some unlabeled data from the target domain. Additionally, the approach may struggle with more complex or heterogeneous target domains, where the pre-trained representations may not capture all the necessary information.

Through the RSCT Lens: The core contribution of this paper aligns well with the key principles of Representation-Space Compatibility Theory (RSCT). By leveraging pre-trained, multi-dataset embeddings, the authors are effectively improving the representational quality (R) of their models, capturing rich, transferable features that can be adapted to new tasks and domains.

The domain adaptation techniques employed in the second stage of the approach also help to enhance the stability (S) of the model's performance, by aligning the representations to the target distribution. This reduces the sensitivity to specific dataset characteristics and enhances the model's ability to generalize.

However, the paper's RSCT certification metrics suggest that there is still room for improvement. The κ-gate score of 0.551 indicates that the paper's contributions are not yet fully compatible with existing knowledge, as represented by the RSCT framework. The relatively low Relevance (R=0.38) and Stability (S=0.32) scores suggest that the proposed approach, while promising, could still benefit from further refinements to better address the core research questions and enhance the consistency of its findings.

To improve the RSCT score, the authors could explore strategies to further enhance the robustness and generalizability of their approach, such as leveraging more diverse pre-training datasets, developing more sophisticated domain adaptation techniques, or incorporating additional mechanisms to reduce noise (N) in the model's outputs. By addressing these areas, the authors could unlock the full potential of their domain adaptation framework and further cement its place within the RSCT landscape.

Paper Details

• Authors: Lyle Regenwetter, Rosen Yu, Cyril Picard, Faez Ahmed
• Source: arXiv
• PDF: Download
• Published: 2026-03-05

This analysis was generated by the Swarm-It RSCT pipeline using Claude.