Multitask-Informed Prior for In-Context Learning on Tabular Data: Application to Steel Property Prediction

• URL: http://arxiv.org/abs/2603.22738v1
• Date: Tue, 24 Mar 2026 03:03:54 GMT
• Title: Multitask-Informed Prior for In-Context Learning on Tabular Data: Application to Steel Property Prediction
• Abstract summary: We present a multitask learning framework that injects multitask awareness into the prior of TabPFN.<n>Our approach enhances both predictive accuracy and computational efficiency compared to task-specific fine-tuning.
• Score: 10.228135528637411
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Accurate prediction of mechanical properties of steel during hot rolling processes, such as Thin Slab Direct Rolling (TSDR), remains challenging due to complex interactions among chemical compositions, processing parameters, and resultant microstructures. Traditional empirical and experimental methodologies, while effective, are often resource-intensive and lack adaptability to varied production conditions. Moreover, most existing approaches do not explicitly leverage the strong correlations among key mechanical properties, missing an opportunity to improve predictive accuracy through multitask learning. To address this, we present a multitask learning framework that injects multitask awareness into the prior of TabPFN--a transformer-based foundation model for in-context learning on tabular data--through novel fine-tuning strategies. Originally designed for single-target regression or classification, we augment TabPFN's prior with two complementary approaches: (i) target averaging, which provides a unified scalar signal compatible with TabPFN's single-target architecture, and (ii) task-specific adapters, which introduce task-specific supervision during fine-tuning. These strategies jointly guide the model toward a multitask-informed prior that captures cross-property relationships among key mechanical metrics. Extensive experiments on an industrial TSDR dataset demonstrate that our multitask adaptations outperform classical machine learning methods and recent state-of-the-art tabular learning models across multiple evaluation metrics. Notably, our approach enhances both predictive accuracy and computational efficiency compared to task-specific fine-tuning, demonstrating that multitask-aware prior adaptation enables foundation models for tabular data to deliver scalable, rapid, and reliable deployment for automated industrial quality control and process optimization in TSDR.

Related papers

• Learning Multi-Indicator Weights for Data Selection: A Joint Task-Model Adaptation Framework with Efficient Proxies [50.39041754816285]
  We propose a framework for learning multi-validation weights that jointly adapts data selection to both the downstream task and the specific model.<n>Our approach achieves performance comparable to or exceeding full-dataset tuning while using only 30% of the training samples on GSM8K.
  arXiv  Detail & Related papers  (2026-05-10T17:30:16Z)
• TableMind: An Autonomous Programmatic Agent for Tool-Augmented Table Reasoning [10.267950603662776]
  TableMind is a tool-integrated table reasoning agent that autonomously performs multi-turn tool invocation, writes and executes code in a secure sandbox environment for data analysis and precise numerical reasoning.<n>To realize these capabilities, we adopt a two-stage fine-tuning paradigm built on top of a powerful pre-trained language model.
  arXiv  Detail & Related papers  (2025-09-08T02:00:31Z)
• Efficient Federated Class-Incremental Learning of Pre-Trained Models via Task-agnostic Low-rank Residual Adaptation [22.454292668849035]
  Federated Task-agnostic Low-rank Residual Adaptation (Fed-TaLoRA)<n>We develop a novel residual weight update mechanism that ensures accurate knowledge consolidation with minimal overhead.<n>Our methodological innovations are attributed to three key strategies: task-agnostic adaptation, post-aggregation model calibration, and strategic placement of LoRA modules.
  arXiv  Detail & Related papers  (2025-05-18T09:19:13Z)
• Meta-Statistical Learning: Supervised Learning of Statistical Inference [59.463430294611626]
  This work demonstrates that the tools and principles driving the success of large language models (LLMs) can be repurposed to tackle distribution-level tasks.<n>We propose meta-statistical learning, a framework inspired by multi-instance learning that reformulates statistical inference tasks as supervised learning problems.
  arXiv  Detail & Related papers  (2025-02-17T18:04:39Z)
• Model Predictive Task Sampling for Efficient and Robust Adaptation [57.414812940406996]
  We introduce Model Predictive Task Sampling (MPTS), a framework that bridges the task space and adaptation risk distributions.<n>MPTS employs a generative model to characterize the episodic optimization process and predicts task-specific adaptation risk via posterior inference.<n>MPTS seamlessly integrates into zero-shot, few-shot, and supervised finetuning settings.
  arXiv  Detail & Related papers  (2025-01-19T13:14:53Z)
• Reprogramming Distillation for Medical Foundation Models [37.52464627899668]
  We propose a novel framework called Reprogramming Distillation (RD) RD reprograms the original feature space of the foundation model so that it is more relevant to downstream scenarios. RD consistently achieve superior performance compared with previous PEFT and KD methods.
  arXiv  Detail & Related papers  (2024-07-09T02:17:51Z)
• Analysing Multi-Task Regression via Random Matrix Theory with Application to Time Series Forecasting [16.640336442849282]
  We formulate a multi-task optimization problem as a regularization technique to enable single-task models to leverage multi-task learning information. We derive a closed-form solution for multi-task optimization in the context of linear models.
  arXiv  Detail & Related papers  (2024-06-14T17:59:25Z)
• When Parameter-efficient Tuning Meets General-purpose Vision-language Models [65.19127815275307]
  PETAL revolutionizes the training process by requiring only 0.5% of the total parameters, achieved through a unique mode approximation technique. Our experiments reveal that PETAL not only outperforms current state-of-the-art methods in most scenarios but also surpasses full fine-tuning models in effectiveness.
  arXiv  Detail & Related papers  (2023-12-16T17:13:08Z)
• Task-Distributionally Robust Data-Free Meta-Learning [99.56612787882334]
  Data-Free Meta-Learning (DFML) aims to efficiently learn new tasks by leveraging multiple pre-trained models without requiring their original training data. For the first time, we reveal two major challenges hindering their practical deployments: Task-Distribution Shift ( TDS) and Task-Distribution Corruption (TDC)
  arXiv  Detail & Related papers  (2023-11-23T15:46:54Z)
• TACTiS-2: Better, Faster, Simpler Attentional Copulas for Multivariate Time Series [57.4208255711412]
  Building on copula theory, we propose a simplified objective for the recently-introduced transformer-based attentional copulas (TACTiS) We show that the resulting model has significantly better training dynamics and achieves state-of-the-art performance across diverse real-world forecasting tasks.
  arXiv  Detail & Related papers  (2023-10-02T16:45:19Z)
• End-to-End Meta-Bayesian Optimisation with Transformer Neural Processes [52.818579746354665]
  This paper proposes the first end-to-end differentiable meta-BO framework that generalises neural processes to learn acquisition functions via transformer architectures. We enable this end-to-end framework with reinforcement learning (RL) to tackle the lack of labelled acquisition data.
  arXiv  Detail & Related papers  (2023-05-25T10:58:46Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.